Project import generated by Copybara.

GitOrigin-RevId: ff83882955f1a1e2a043ff4e71278be9d7217bbe

Dockerfile

@@ -23,6 +23,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
         build-essential \
+        gcc-8 g++-8 \
         ca-certificates \
         curl \
         ffmpeg \
@@ -44,6 +45,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100 --slave /usr/bin/g++ g++ /usr/bin/g++-8
 RUN pip3 install --upgrade setuptools
 RUN pip3 install wheel
 RUN pip3 install future

WORKSPACE

@@ -337,6 +337,8 @@ maven_install(
         "androidx.test.espresso:espresso-core:3.1.1",
         "com.github.bumptech.glide:glide:4.11.0",
         "com.google.android.material:material:aar:1.0.0-rc01",
+        "com.google.auto.value:auto-value:1.6.4",
+        "com.google.auto.value:auto-value-annotations:1.6.4",
         "com.google.code.findbugs:jsr305:3.0.2",
         "com.google.flogger:flogger-system-backend:0.3.1",
         "com.google.flogger:flogger:0.3.1",
@@ -367,9 +369,9 @@ http_archive(
 )
 
 # Tensorflow repo should always go after the other external dependencies.
-# 2021-03-25
+# 2021-04-30
-_TENSORFLOW_GIT_COMMIT = "c67f68021824410ebe9f18513b8856ac1c6d4887"
+_TENSORFLOW_GIT_COMMIT = "5bd3c57ef184543d22e34e36cff9d9bea608e06d"
-_TENSORFLOW_SHA256= "fd07d0b39422dc435e268c5e53b2646a8b4b1e3151b87837b43f86068faae87f"
+_TENSORFLOW_SHA256= "9a45862834221aafacf6fb275f92b3876bc89443cbecc51be93f13839a6609f0"
 http_archive(
     name = "org_tensorflow",
     urls = [

build_desktop_examples.sh

@@ -17,15 +17,15 @@
 # Script to build/run all MediaPipe desktop example apps (with webcam input).
 #
 # To build and run all apps and store them in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir
+#   $ ./build_desktop_examples.sh -d out_dir
 #   Omitting -d and the associated directory saves all generated apps in the
 #   current directory.
 # To build all apps and store them in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir -b
+#   $ ./build_desktop_examples.sh -d out_dir -b
 #   Omitting -d and the associated directory saves all generated apps in the
 #   current directory.
 # To run all apps already stored in out_dir:
-#   $ ./build_ios_examples.sh -d out_dir -r
+#   $ ./build_desktop_examples.sh -d out_dir -r
 #   Omitting -d and the associated directory assumes all apps are in the current
 #   directory.
 

docs/framework_concepts/calculators.md

@@ -187,7 +187,7 @@ node {
 ```
 
 In the calculator implementation, inputs and outputs are also identified by tag
-name and index number. In the function below input are output are identified:
+name and index number. In the function below input and output are identified:
 
 *   By index number: The combined input stream is identified simply by index
     `0`.
@@ -355,7 +355,6 @@ class PacketClonerCalculator : public CalculatorBase {
               current_[i].At(cc->InputTimestamp()));
           // Add a packet to output stream of index i a packet from inputstream i
           // with timestamp common to all present inputs
-          //
         } else {
           cc->Outputs().Index(i).SetNextTimestampBound(
               cc->InputTimestamp().NextAllowedInStream());
@@ -382,7 +381,7 @@ defined your calculator class, register it with a macro invocation
 REGISTER_CALCULATOR(calculator_class_name).
 
 Below is a trivial MediaPipe graph that has 3 input streams, 1 node
-(PacketClonerCalculator) and 3 output streams.
+(PacketClonerCalculator) and 2 output streams.
 
 ```proto
 input_stream: "room_mic_signal"

docs/framework_concepts/graphs.md

@@ -83,12 +83,12 @@ Below is an example of how to create a subgraph named `TwoPassThroughSubgraph`.
     output_stream: "out3"
 
     node {
-        calculator: "PassThroughculator"
+        calculator: "PassThroughCalculator"
         input_stream: "out1"
         output_stream: "out2"
     }
     node {
-        calculator: "PassThroughculator"
+        calculator: "PassThroughCalculator"
         input_stream: "out2"
         output_stream: "out3"
     }

docs/getting_started/android.md

@@ -57,7 +57,7 @@ Please verify all the necessary packages are installed.
 *   Android SDK Build-Tools 28 or 29
 *   Android SDK Platform-Tools 28 or 29
 *   Android SDK Tools 26.1.1
-*   Android NDK 17c or above
+*   Android NDK 19c or above
 
 ### Option 1: Build with Bazel in Command Line
 
@@ -111,7 +111,7 @@ app:
     *   Verify that Android SDK Build-Tools 28 or 29 is installed.
     *   Verify that Android SDK Platform-Tools 28 or 29 is installed.
     *   Verify that Android SDK Tools 26.1.1 is installed.
-    *   Verify that Android NDK 17c or above is installed.
+    *   Verify that Android NDK 19c or above is installed.
     *   Take note of the Android NDK Location, e.g.,
         `/usr/local/home/Android/Sdk/ndk-bundle` or
         `/usr/local/home/Android/Sdk/ndk/20.0.5594570`.

docs/getting_started/android_archive_library.md

@@ -37,7 +37,7 @@ each project.
     load("//mediapipe/java/com/google/mediapipe:mediapipe_aar.bzl", "mediapipe_aar")
 
     mediapipe_aar(
-        name = "mp_face_detection_aar",
+        name = "mediapipe_face_detection",
         calculators = ["//mediapipe/graphs/face_detection:mobile_calculators"],
     )
     ```
@@ -45,26 +45,29 @@ each project.
 2.  Run the Bazel build command to generate the AAR.
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+    bazel build -c opt --strip=ALWAYS \
-        --fat_apk_cpu=arm64-v8a,armeabi-v7a --strip=ALWAYS \
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
-        //path/to/the/aar/build/file:aar_name
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //path/to/the/aar/build/file:aar_name.aar
     ```
 
-    For the face detection AAR target we made in the step 1, run:
+    For the face detection AAR target we made in step 1, run:
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+    bazel build -c opt --strip=ALWAYS \
-        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
 
     # It should print:
-    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar up-to-date:
+    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
-    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     ```
 
 3.  (Optional) Save the AAR to your preferred location.
 
     ```bash
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     /absolute/path/to/your/preferred/location
     ```
 
@@ -75,7 +78,7 @@ each project.
 2.  Copy the AAR into app/libs.
 
     ```bash
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     /path/to/your/app/libs/
     ```
 
@@ -92,29 +95,14 @@ each project.
     [the face detection tflite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite).
 
     ```bash
-    bazel build -c opt mediapipe/mediapipe/graphs/face_detection:mobile_gpu_binary_graph
+    bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
-    cp bazel-bin/mediapipe/graphs/face_detection/mobile_gpu.binarypb /path/to/your/app/src/main/assets/
+    cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
     cp mediapipe/modules/face_detection/face_detection_front.tflite /path/to/your/app/src/main/assets/
     ```
 
     ![Screenshot](../images/mobile/assets_location.png)
 
-4.  Make app/src/main/jniLibs and copy OpenCV JNI libraries into
+4.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
-    app/src/main/jniLibs.
-
-    MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so
-    files into app/src/main/jniLibs. You can download the official OpenCV
-    Android SDK from
-    [here](https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip)
-    and run:
-
-    ```bash
-    cp -R ~/Downloads/OpenCV-android-sdk/sdk/native/libs/arm* /path/to/your/app/src/main/jniLibs/
-    ```
-
-    ![Screenshot](../images/mobile/android_studio_opencv_location.png)
-
-5.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
 
     ```
     dependencies {
@@ -136,10 +124,14 @@ each project.
         implementation "androidx.camera:camera-core:$camerax_version"
         implementation "androidx.camera:camera-camera2:$camerax_version"
         implementation "androidx.camera:camera-lifecycle:$camerax_version"
+        // AutoValue
+        def auto_value_version = "1.6.4"
+        implementation "com.google.auto.value:auto-value-annotations:$auto_value_version"
+        annotationProcessor "com.google.auto.value:auto-value:$auto_value_version"
     }
     ```
 
-6.  Follow our Android app examples to use MediaPipe in Android Studio for your
+5.  Follow our Android app examples to use MediaPipe in Android Studio for your
     use case. If you are looking for an example, a face detection example can be
     found
     [here](https://github.com/jiuqiant/mediapipe_face_detection_aar_example) and

docs/getting_started/install.md

@@ -471,7 +471,7 @@ next section.
 4.  Install Visual C++ Build Tools 2019 and WinSDK
 
     Go to
-    [the VisualStudio website](ttps://visualstudio.microsoft.com/visual-cpp-build-tools),
+    [the VisualStudio website](https://visualstudio.microsoft.com/visual-cpp-build-tools),
     download build tools, and install Microsoft Visual C++ 2019 Redistributable
     and Microsoft Build Tools 2019.
 
@@ -738,7 +738,7 @@ common build issues.
     root@bca08b91ff63:/mediapipe# bash ./setup_android_sdk_and_ndk.sh
 
     # Should print:
-    # Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r18b
+    # Android NDK is now installed. Consider setting $ANDROID_NDK_HOME environment variable to be /root/Android/Sdk/ndk-bundle/android-ndk-r19c
     # Set android_ndk_repository and android_sdk_repository in WORKSPACE
     # Done
 

docs/getting_started/python.md

@@ -26,7 +26,7 @@ You can, for instance, activate a Python virtual environment:
 $ python3 -m venv mp_env && source mp_env/bin/activate
 ```
 
-Install MediaPipe Python package and start Python intepreter:
+Install MediaPipe Python package and start Python interpreter:
 
 ```bash
 (mp_env)$ pip install mediapipe

docs/getting_started/troubleshooting.md

@@ -97,6 +97,49 @@ linux_opencv/macos_opencv/windows_opencv.BUILD files for your local opencv
 libraries. [This GitHub issue](https://github.com/google/mediapipe/issues/666)
 may also help.
 
+## Python pip install failure
+
+The error message:
+
+```
+ERROR: Could not find a version that satisfies the requirement mediapipe
+ERROR: No matching distribution found for mediapipe
+```
+
+after running `pip install mediapipe` usually indicates that there is no qualified MediaPipe Python for your system.
+Please note that MediaPipe Python PyPI officially supports the **64-bit**
+version of Python 3.7 and above on the following OS:
+
+-   x86_64 Linux
+-   x86_64 macOS 10.15+
+-   amd64 Windows
+
+If the OS is currently supported and you still see this error, please make sure
+that both the Python and pip binary are for Python 3.7 and above. Otherwise,
+please consider building the MediaPipe Python package locally by following the
+instructions [here](python.md#building-mediapipe-python-package).
+
+## Python DLL load failure on Windows
+
+The error message:
+
+```
+ImportError: DLL load failed: The specified module could not be found
+```
+
+usually indicates that the local Windows system is missing Visual C++
+redistributable packages and/or Visual C++ runtime DLLs. This can be solved by
+either installing the official
+[vc_redist.x64.exe](https://support.microsoft.com/en-us/topic/the-latest-supported-visual-c-downloads-2647da03-1eea-4433-9aff-95f26a218cc0)
+or installing the "msvc-runtime" Python package by running
+
+```bash
+$ python -m pip install msvc-runtime
+```
+
+Please note that the "msvc-runtime" Python package is not released or maintained
+by Microsoft.
+
 ## Native method not found
 
 The error message:

docs/solutions/face_detection.md

@@ -77,7 +77,7 @@ Supported configuration options:
 ```python
 import cv2
 import mediapipe as mp
-mp_face_detction = mp.solutions.face_detection
+mp_face_detection = mp.solutions.face_detection
 mp_drawing = mp.solutions.drawing_utils
 
 # For static images:

docs/solutions/holistic.md

@@ -135,12 +135,11 @@ another detection until it loses track, on reducing computation and latency. If
 set to `true`, person detection runs every input image, ideal for processing a
 batch of static, possibly unrelated, images. Default to `false`.
 
-#### upper_body_only
+#### model_complexity
 
-If set to `true`, the solution outputs only the 25 upper-body pose landmarks
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
-(535 in total) instead of the full set of 33 pose landmarks (543 in total). Note
+well as inference latency generally go up with the model complexity. Default to
-that upper-body-only prediction may be more accurate for use cases where the
+`1`.
-lower-body parts are mostly out of view. Default to `false`.
 
 #### smooth_landmarks
 
@@ -207,7 +206,7 @@ install MediaPipe Python package, then learn more in the companion
 Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
-*   [upper_body_only](#upper_body_only)
+*   [model_complexity](#model_complexity)
 *   [smooth_landmarks](#smooth_landmarks)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
@@ -219,7 +218,9 @@ mp_drawing = mp.solutions.drawing_utils
 mp_holistic = mp.solutions.holistic
 
 # For static images:
-with mp_holistic.Holistic(static_image_mode=True) as holistic:
+with mp_holistic.Holistic(
+    static_image_mode=True,
+    model_complexity=2) as holistic:
   for idx, file in enumerate(file_list):
     image = cv2.imread(file)
     image_height, image_width, _ = image.shape
@@ -240,8 +241,6 @@ with mp_holistic.Holistic(static_image_mode=True) as holistic:
         annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
     mp_drawing.draw_landmarks(
         annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
-    # Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when
-    # upper_body_only is set to True.
     mp_drawing.draw_landmarks(
         annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
     cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
@@ -291,7 +290,7 @@ and the following usage example.
 
 Supported configuration options:
 
-*   [upperBodyOnly](#upper_body_only)
+*   [modelComplexity](#model_complexity)
 *   [smoothLandmarks](#smooth_landmarks)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
@@ -348,7 +347,7 @@ const holistic = new Holistic({locateFile: (file) => {
   return `https://cdn.jsdelivr.net/npm/@mediapipe/holistic/${file}`;
 }});
 holistic.setOptions({
-  upperBodyOnly: false,
+  modelComplexity: 1,
   smoothLandmarks: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5

docs/solutions/models.md

@@ -15,10 +15,10 @@ nav_order: 30
 ### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
 
 *   Face detection model for front-facing/selfie camera:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_front.tflite),
+    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite),
     [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite)
 *   Face detection model for back-facing camera:
-    [TFLite model ](https://github.com/google/mediapipe/tree/master/mediapipe/models/face_detection_back.tflite)
+    [TFLite model ](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_back.tflite)
 *   [Model card](https://mediapipe.page.link/blazeface-mc)
 
 ### [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)
@@ -49,10 +49,10 @@ nav_order: 30
 
 *   Pose detection model:
     [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection.tflite)
-*   Full-body pose landmark model:
+*   Pose landmark model:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full_body.tflite)
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite),
-*   Upper-body pose landmark model:
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full.tflite),
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite)
+    [TFLite model (heavy)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite)
 *   [Model card](https://mediapipe.page.link/blazepose-mc)
 
 ### [Holistic](https://google.github.io/mediapipe/solutions/holistic)

docs/solutions/pose.md

@@ -30,8 +30,7 @@ overlay of digital content and information on top of the physical world in
 augmented reality.
 
 MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring
-33 3D landmarks on the whole body (or 25 upper-body landmarks) from RGB video
+33 3D landmarks on the whole body from RGB video frames utilizing our
-frames utilizing our
 [BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
 research that also powers the
 [ML Kit Pose Detection API](https://developers.google.com/ml-kit/vision/pose-detection).
@@ -40,9 +39,9 @@ environments for inference, whereas our method achieves real-time performance on
 most modern [mobile phones](#mobile), [desktops/laptops](#desktop), in
 [python](#python-solution-api) and even on the [web](#javascript-solution-api).
 
-![pose_tracking_upper_body_example.gif](../images/mobile/pose_tracking_upper_body_example.gif) |
+![pose_tracking_example.gif](../images/mobile/pose_tracking_example.gif) |
-:--------------------------------------------------------------------------------------------: |
+:----------------------------------------------------------------------: |
-*Fig 1. Example of MediaPipe Pose for upper-body pose tracking.*                               |
+*Fig 1. Example of MediaPipe Pose for pose tracking.*                    |
 
 ## ML Pipeline
 
@@ -77,6 +76,23 @@ Note: To visualize a graph, copy the graph and paste it into
 to visualize its associated subgraphs, please see
 [visualizer documentation](../tools/visualizer.md).
 
+## Pose Estimation Quality
+
+To evaluate the quality of our [models](./models.md#pose) against other
+well-performing publicly available solutions, we use a validation dataset,
+consisting of 1k images with diverse Yoga, HIIT, and Dance postures. Each image
+contains only a single person located 2-4 meters from the camera. To be
+consistent with other solutions, we perform evaluation only for 17 keypoints
+from [COCO topology](https://cocodataset.org/#keypoints-2020).
+
+Method                                                                                                | [mAP](https://cocodataset.org/#keypoints-eval) | [PCK@0.2](https://github.com/cbsudux/Human-Pose-Estimation-101) | [FPS](https://en.wikipedia.org/wiki/Frame_rate), Pixel 3 [TFLite GPU](https://www.tensorflow.org/lite/performance/gpu_advanced) | [FPS](https://en.wikipedia.org/wiki/Frame_rate), MacBook Pro (15-inch, 2017)
+----------------------------------------------------------------------------------------------------- | ---------------------------------------------: | --------------------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------: | ---------------------------------------------------------------------------:
+BlazePose.Lite                                                                                        | 49.1                                           | 91.7                                                            | 49                                                                                                                              | 40
+BlazePose.Full                                                                                        | 64.5                                           | 95.8                                                            | 40                                                                                                                              | 37
+BlazePose.Heavy                                                                                       | 70.9                                           | 97.0                                                            | 19                                                                                                                              | 26
+[AlphaPose.ResNet50](https://github.com/MVIG-SJTU/AlphaPose)                                          | 57.6                                           | 93.1                                                            | N/A                                                                                                                             | N/A
+[Apple Vision](https://developer.apple.com/documentation/vision/detecting_human_body_poses_in_images) | 37.0                                           | 85.3                                                            | N/A                                                                                                                             | N/A
+
 ## Models
 
 ### Person/pose Detection Model (BlazePose Detector)
@@ -97,11 +113,8 @@ hip midpoints.
 
 ### Pose Landmark Model (BlazePose GHUM 3D)
 
-The landmark model in MediaPipe Pose comes in two versions: a full-body model
+The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
-that predicts the location of 33 pose landmarks (see figure below), and an
+(see figure below).
-upper-body version that only predicts the first 25. The latter may be more
-accurate than the former in scenarios where the lower-body parts are mostly out
-of view.
 
 Please find more detail in the
 [BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html),
@@ -129,12 +142,11 @@ until it loses track, on reducing computation and latency. If set to `true`,
 person detection runs every input image, ideal for processing a batch of static,
 possibly unrelated, images. Default to `false`.
 
-#### upper_body_only
+#### model_complexity
 
-If set to `true`, the solution outputs only the 25 upper-body pose landmarks.
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
-Otherwise, it outputs the full set of 33 pose landmarks. Note that
+well as inference latency generally go up with the model complexity. Default to
-upper-body-only prediction may be more accurate for use cases where the
+`1`.
-lower-body parts are mostly out of view. Default to `false`.
 
 #### smooth_landmarks
 
@@ -170,9 +182,6 @@ A list of pose landmarks. Each lanmark consists of the following:
     being the origin, and the smaller the value the closer the landmark is to
     the camera. The magnitude of `z` uses roughly the same scale as `x`.
 
-    Note: `z` is predicted only in full-body mode, and should be discarded when
-    [upper_body_only](#upper_body_only) is `true`.
-
 *   `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
     landmark being visible (present and not occluded) in the image.
 
@@ -185,7 +194,7 @@ install MediaPipe Python package, then learn more in the companion
 Supported configuration options:
 
 *   [static_image_mode](#static_image_mode)
-*   [upper_body_only](#upper_body_only)
+*   [model_complexity](#model_complexity)
 *   [smooth_landmarks](#smooth_landmarks)
 *   [min_detection_confidence](#min_detection_confidence)
 *   [min_tracking_confidence](#min_tracking_confidence)
@@ -198,7 +207,9 @@ mp_pose = mp.solutions.pose
 
 # For static images:
 with mp_pose.Pose(
-    static_image_mode=True, min_detection_confidence=0.5) as pose:
+    static_image_mode=True,
+    model_complexity=2,
+    min_detection_confidence=0.5) as pose:
   for idx, file in enumerate(file_list):
     image = cv2.imread(file)
     image_height, image_width, _ = image.shape
@@ -214,8 +225,6 @@ with mp_pose.Pose(
     )
     # Draw pose landmarks on the image.
     annotated_image = image.copy()
-    # Use mp_pose.UPPER_BODY_POSE_CONNECTIONS for drawing below when
-    # upper_body_only is set to True.
     mp_drawing.draw_landmarks(
         annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
     cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
@@ -259,7 +268,7 @@ and the following usage example.
 
 Supported configuration options:
 
-*   [upperBodyOnly](#upper_body_only)
+*   [modelComplexity](#model_complexity)
 *   [smoothLandmarks](#smooth_landmarks)
 *   [minDetectionConfidence](#min_detection_confidence)
 *   [minTrackingConfidence](#min_tracking_confidence)
@@ -306,7 +315,7 @@ const pose = new Pose({locateFile: (file) => {
   return `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`;
 }});
 pose.setOptions({
-  upperBodyOnly: false,
+  modelComplexity: 1,
   smoothLandmarks: true,
   minDetectionConfidence: 0.5,
   minTrackingConfidence: 0.5
@@ -347,16 +356,6 @@ to visualize its associated subgraphs, please see
 *   iOS target:
     [`mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp`](http:/mediapipe/examples/ios/posetrackinggpu/BUILD)
 
-#### Upper-body Only
-
-*   Graph:
-    [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
-*   Android target:
-    [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1uKc6T7KSuA0Mlq2URi5YookHu0U3yoh_/view?usp=sharing)
-    [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu:upperbodyposetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD)
-*   iOS target:
-    [`mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp`](http:/mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD)
-
 ### Desktop
 
 Please first see general instructions for [desktop](../getting_started/cpp.md)
@@ -375,19 +374,6 @@ on how to build MediaPipe examples.
     *   Target:
         [`mediapipe/examples/desktop/pose_tracking:pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
 
-#### Upper-body Only
-
-*   Running on CPU
-    *   Graph:
-        [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt)
-    *   Target:
-        [`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
-*   Running on GPU
-    *   Graph:
-        [`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
-    *   Target:
-        [`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
-
 ## Resources
 
 *   Google AI Blog:

mediapipe/MediaPipe.tulsiproj/Configs/MediaPipe.tulsigen

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

mediapipe/MediaPipe.tulsiproj/project.tulsiconf

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

mediapipe/calculators/core/BUILD

@@ -451,8 +451,8 @@ cc_library(
 )
 
 cc_library(
-    name = "nonzero_calculator",
+    name = "non_zero_calculator",
-    srcs = ["nonzero_calculator.cc"],
+    srcs = ["non_zero_calculator.cc"],
     visibility = [
         "//visibility:public",
     ],
@@ -464,6 +464,21 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_test(
+    name = "non_zero_calculator_test",
+    size = "small",
+    srcs = ["non_zero_calculator_test.cc"],
+    deps = [
+        ":non_zero_calculator",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework:timestamp",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:status",
+        "//mediapipe/framework/tool:validate_type",
+    ],
+)
+
 cc_test(
     name = "mux_calculator_test",
     srcs = ["mux_calculator_test.cc"],
@@ -665,6 +680,18 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "default_side_packet_calculator",
+    srcs = ["default_side_packet_calculator.cc"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/port:ret_check",
+        "//mediapipe/framework/port:status",
+    ],
+    alwayslink = 1,
+)
+
 cc_library(
     name = "side_packet_to_stream_calculator",
     srcs = ["side_packet_to_stream_calculator.cc"],

mediapipe/calculators/core/default_side_packet_calculator.cc

@@ -0,0 +1,103 @@
+// Copyright 2019 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/port/ret_check.h"
+#include "mediapipe/framework/port/status.h"
+
+namespace mediapipe {
+
+namespace {
+
+constexpr char kOptionalValueTag[] = "OPTIONAL_VALUE";
+constexpr char kDefaultValueTag[] = "DEFAULT_VALUE";
+constexpr char kValueTag[] = "VALUE";
+
+}  // namespace
+
+// Outputs side packet default value if optional value is not provided.
+//
+// This calculator utilizes the fact that MediaPipe automatically removes
+// optional side packets of the calculator configuration (i.e. OPTIONAL_VALUE).
+// And if it happens - returns default value, otherwise - returns optional
+// value.
+//
+// Input:
+//   OPTIONAL_VALUE (optional) - AnyType (but same type as DEFAULT_VALUE)
+//     Optional side packet value that is outputted by the calculator as is if
+//     provided.
+//
+//   DEFAULT_VALUE - AnyType
+//     Default side pack value that is outputted by the calculator if
+//     OPTIONAL_VALUE is not provided.
+//
+// Output:
+//   VALUE - AnyType (but same type as DEFAULT_VALUE)
+//     Either OPTIONAL_VALUE (if provided) or DEFAULT_VALUE (otherwise).
+//
+// Usage example:
+//   node {
+//     calculator: "DefaultSidePacketCalculator"
+//     input_side_packet: "OPTIONAL_VALUE:segmentation_mask_enabled_optional"
+//     input_side_packet: "DEFAULT_VALUE:segmentation_mask_enabled_default"
+//     output_side_packet: "VALUE:segmentation_mask_enabled"
+//   }
+class DefaultSidePacketCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc);
+  absl::Status Open(CalculatorContext* cc) override;
+  absl::Status Process(CalculatorContext* cc) override;
+};
+REGISTER_CALCULATOR(DefaultSidePacketCalculator);
+
+absl::Status DefaultSidePacketCalculator::GetContract(CalculatorContract* cc) {
+  RET_CHECK(cc->InputSidePackets().HasTag(kDefaultValueTag))
+      << "Default value must be provided";
+  cc->InputSidePackets().Tag(kDefaultValueTag).SetAny();
+
+  // Optional input side packet can be unspecified. In this case MediaPipe will
+  // remove it from the calculator config.
+  if (cc->InputSidePackets().HasTag(kOptionalValueTag)) {
+    cc->InputSidePackets()
+        .Tag(kOptionalValueTag)
+        .SetSameAs(&cc->InputSidePackets().Tag(kDefaultValueTag));
+  }
+
+  RET_CHECK(cc->OutputSidePackets().HasTag(kValueTag));
+  cc->OutputSidePackets().Tag(kValueTag).SetSameAs(
+      &cc->InputSidePackets().Tag(kDefaultValueTag));
+
+  return absl::OkStatus();
+}
+
+absl::Status DefaultSidePacketCalculator::Open(CalculatorContext* cc) {
+  // If optional value is provided it is returned as the calculator output.
+  if (cc->InputSidePackets().HasTag(kOptionalValueTag)) {
+    auto& packet = cc->InputSidePackets().Tag(kOptionalValueTag);
+    cc->OutputSidePackets().Tag(kValueTag).Set(packet);
+    return absl::OkStatus();
+  }
+
+  // If no optional value
+  auto& packet = cc->InputSidePackets().Tag(kDefaultValueTag);
+  cc->OutputSidePackets().Tag(kValueTag).Set(packet);
+
+  return absl::OkStatus();
+}
+
+absl::Status DefaultSidePacketCalculator::Process(CalculatorContext* cc) {
+  return absl::OkStatus();
+}
+
+}  // namespace mediapipe

mediapipe/calculators/core/non_zero_calculator.cc

@@ -23,14 +23,26 @@ namespace api2 {
 class NonZeroCalculator : public Node {
  public:
   static constexpr Input<int>::SideFallback kIn{"INPUT"};
-  static constexpr Output<int> kOut{"OUTPUT"};
+  static constexpr Output<int>::Optional kOut{"OUTPUT"};
+  static constexpr Output<bool>::Optional kBooleanOut{"OUTPUT_BOOL"};
 
-  MEDIAPIPE_NODE_CONTRACT(kIn, kOut);
+  MEDIAPIPE_NODE_CONTRACT(kIn, kOut, kBooleanOut);
+
+  absl::Status UpdateContract(CalculatorContract* cc) {
+    RET_CHECK(kOut(cc).IsConnected() || kBooleanOut(cc).IsConnected())
+        << "At least one output stream is expected.";
+    return absl::OkStatus();
+  }
 
   absl::Status Process(CalculatorContext* cc) final {
     if (!kIn(cc).IsEmpty()) {
-      auto output = std::make_unique<int>((*kIn(cc) != 0) ? 1 : 0);
+      bool isNonZero = *kIn(cc) != 0;
-      kOut(cc).Send(std::move(output));
+      if (kOut(cc).IsConnected()) {
+        kOut(cc).Send(std::make_unique<int>(isNonZero ? 1 : 0));
+      }
+      if (kBooleanOut(cc).IsConnected()) {
+        kBooleanOut(cc).Send(std::make_unique<bool>(isNonZero));
+      }
     }
     return absl::OkStatus();
   }

mediapipe/calculators/core/non_zero_calculator_test.cc

@@ -0,0 +1,93 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_runner.h"
+#include "mediapipe/framework/port/canonical_errors.h"
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/status.h"
+#include "mediapipe/framework/port/status_matchers.h"
+#include "mediapipe/framework/timestamp.h"
+#include "mediapipe/framework/tool/validate_type.h"
+
+namespace mediapipe {
+
+class NonZeroCalculatorTest : public ::testing::Test {
+ protected:
+  NonZeroCalculatorTest()
+      : runner_(
+            R"pb(
+              calculator: "NonZeroCalculator"
+              input_stream: "INPUT:input"
+              output_stream: "OUTPUT:output"
+              output_stream: "OUTPUT_BOOL:output_bool"
+            )pb") {}
+
+  void SetInput(const std::vector<int>& inputs) {
+    int timestamp = 0;
+    for (const auto input : inputs) {
+      runner_.MutableInputs()
+          ->Get("INPUT", 0)
+          .packets.push_back(MakePacket<int>(input).At(Timestamp(timestamp++)));
+    }
+  }
+
+  std::vector<int> GetOutput() {
+    std::vector<int> result;
+    for (const auto output : runner_.Outputs().Get("OUTPUT", 0).packets) {
+      result.push_back(output.Get<int>());
+    }
+    return result;
+  }
+
+  std::vector<bool> GetOutputBool() {
+    std::vector<bool> result;
+    for (const auto output : runner_.Outputs().Get("OUTPUT_BOOL", 0).packets) {
+      result.push_back(output.Get<bool>());
+    }
+    return result;
+  }
+
+  CalculatorRunner runner_;
+};
+
+TEST_F(NonZeroCalculatorTest, ProducesZeroOutputForZeroInput) {
+  SetInput({0});
+
+  MP_ASSERT_OK(runner_.Run());
+
+  EXPECT_THAT(GetOutput(), ::testing::ElementsAre(0));
+  EXPECT_THAT(GetOutputBool(), ::testing::ElementsAre(false));
+}
+
+TEST_F(NonZeroCalculatorTest, ProducesNonZeroOutputForNonZeroInput) {
+  SetInput({1, 2, 3, -4, 5});
+
+  MP_ASSERT_OK(runner_.Run());
+
+  EXPECT_THAT(GetOutput(), ::testing::ElementsAre(1, 1, 1, 1, 1));
+  EXPECT_THAT(GetOutputBool(),
+              ::testing::ElementsAre(true, true, true, true, true));
+}
+
+TEST_F(NonZeroCalculatorTest, SwitchesBetweenNonZeroAndZeroOutput) {
+  SetInput({1, 0, 3, 0, 5});
+  MP_ASSERT_OK(runner_.Run());
+  EXPECT_THAT(GetOutput(), ::testing::ElementsAre(1, 0, 1, 0, 1));
+  EXPECT_THAT(GetOutputBool(),
+              ::testing::ElementsAre(true, false, true, false, true));
+}
+
+}  // namespace mediapipe

mediapipe/calculators/image/image_cropping_calculator.cc

@@ -285,7 +285,7 @@ absl::Status ImageCroppingCalculator::RenderGpu(CalculatorContext* cc) {
 
   // Run cropping shader on GPU.
   {
-    gpu_helper_.BindFramebuffer(dst_tex);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(dst_tex);
 
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(src_tex.target(), src_tex.name());

mediapipe/calculators/image/image_transformation_calculator.cc

@@ -546,7 +546,7 @@ absl::Status ImageTransformationCalculator::RenderGpu(CalculatorContext* cc) {
   auto dst = gpu_helper_.CreateDestinationTexture(output_width, output_height,
                                                   input.format());
 
-  gpu_helper_.BindFramebuffer(dst);  // GL_TEXTURE0
+  gpu_helper_.BindFramebuffer(dst);
   glActiveTexture(GL_TEXTURE1);
   glBindTexture(src1.target(), src1.name());
 

mediapipe/calculators/image/recolor_calculator.cc

@@ -209,6 +209,9 @@ absl::Status RecolorCalculator::Close(CalculatorContext* cc) {
 
 absl::Status RecolorCalculator::RenderCpu(CalculatorContext* cc) {
   if (cc->Inputs().Tag(kMaskCpuTag).IsEmpty()) {
+    cc->Outputs()
+        .Tag(kImageFrameTag)
+        .AddPacket(cc->Inputs().Tag(kImageFrameTag).Value());
     return absl::OkStatus();
   }
   // Get inputs and setup output.
@@ -270,6 +273,9 @@ absl::Status RecolorCalculator::RenderCpu(CalculatorContext* cc) {
 
 absl::Status RecolorCalculator::RenderGpu(CalculatorContext* cc) {
   if (cc->Inputs().Tag(kMaskGpuTag).IsEmpty()) {
+    cc->Outputs()
+        .Tag(kGpuBufferTag)
+        .AddPacket(cc->Inputs().Tag(kGpuBufferTag).Value());
     return absl::OkStatus();
   }
 #if !MEDIAPIPE_DISABLE_GPU
@@ -287,7 +293,7 @@ absl::Status RecolorCalculator::RenderGpu(CalculatorContext* cc) {
 
   // Run recolor shader on GPU.
   {
-    gpu_helper_.BindFramebuffer(dst_tex);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(dst_tex);
 
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(img_tex.target(), img_tex.name());

mediapipe/calculators/image/set_alpha_calculator.cc

@@ -323,7 +323,7 @@ absl::Status SetAlphaCalculator::RenderGpu(CalculatorContext* cc) {
     const auto& alpha_mask =
         cc->Inputs().Tag(kInputAlphaTagGpu).Get<mediapipe::GpuBuffer>();
     auto alpha_texture = gpu_helper_.CreateSourceTexture(alpha_mask);
-    gpu_helper_.BindFramebuffer(output_texture);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(output_texture);
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(GL_TEXTURE_2D, input_texture.name());
     glActiveTexture(GL_TEXTURE2);
@@ -335,7 +335,7 @@ absl::Status SetAlphaCalculator::RenderGpu(CalculatorContext* cc) {
     glBindTexture(GL_TEXTURE_2D, 0);
     alpha_texture.Release();
   } else {
-    gpu_helper_.BindFramebuffer(output_texture);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(output_texture);
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(GL_TEXTURE_2D, input_texture.name());
     GlRender(cc);  // use value from options

mediapipe/calculators/tensor/BUILD

@@ -490,6 +490,7 @@ cc_library(
         "//mediapipe/framework/port:statusor",
         "//mediapipe/framework:calculator_framework",
         "//mediapipe/framework:port",
+        "//mediapipe/gpu:gpu_origin_cc_proto",
     ] + select({
         "//mediapipe/gpu:disable_gpu": [],
         "//conditions:default": [":image_to_tensor_calculator_gpu_deps"],
@@ -526,6 +527,7 @@ mediapipe_proto_library(
     deps = [
         "//mediapipe/framework:calculator_options_proto",
         "//mediapipe/framework:calculator_proto",
+        "//mediapipe/gpu:gpu_origin_proto",
     ],
 )
 

mediapipe/calculators/tensor/image_to_tensor_calculator.cc

@@ -31,6 +31,7 @@
 #include "mediapipe/framework/port/ret_check.h"
 #include "mediapipe/framework/port/status.h"
 #include "mediapipe/framework/port/statusor.h"
+#include "mediapipe/gpu/gpu_origin.pb.h"
 
 #if !MEDIAPIPE_DISABLE_GPU
 #include "mediapipe/gpu/gpu_buffer.h"
@@ -236,7 +237,7 @@ class ImageToTensorCalculator : public Node {
   }
 
  private:
-  bool DoesInputStartAtBottom() {
+  bool DoesGpuInputStartAtBottom() {
     return options_.gpu_origin() != mediapipe::GpuOrigin_Mode_TOP_LEFT;
   }
 
@@ -290,11 +291,11 @@ class ImageToTensorCalculator : public Node {
 #elif MEDIAPIPE_OPENGL_ES_VERSION >= MEDIAPIPE_OPENGL_ES_31
         ASSIGN_OR_RETURN(gpu_converter_,
                          CreateImageToGlBufferTensorConverter(
-                             cc, DoesInputStartAtBottom(), GetBorderMode()));
+                             cc, DoesGpuInputStartAtBottom(), GetBorderMode()));
 #else
         ASSIGN_OR_RETURN(gpu_converter_,
                          CreateImageToGlTextureTensorConverter(
-                             cc, DoesInputStartAtBottom(), GetBorderMode()));
+                             cc, DoesGpuInputStartAtBottom(), GetBorderMode()));
 #endif  // MEDIAPIPE_METAL_ENABLED
 #endif  // !MEDIAPIPE_DISABLE_GPU
       }

mediapipe/calculators/tensor/image_to_tensor_calculator.proto

@@ -17,20 +17,7 @@ syntax = "proto2";
 package mediapipe;
 
 import "mediapipe/framework/calculator.proto";
-
+import "mediapipe/gpu/gpu_origin.proto";
-message GpuOrigin {
-  enum Mode {
-    DEFAULT = 0;
-
-    // OpenGL: bottom-left origin
-    // Metal : top-left origin
-    CONVENTIONAL = 1;
-
-    // OpenGL: top-left origin
-    // Metal : top-left origin
-    TOP_LEFT = 2;
-  }
-}
 
 message ImageToTensorCalculatorOptions {
   extend mediapipe.CalculatorOptions {

mediapipe/calculators/tensor/inference_calculator_gl.cc

@@ -317,7 +317,8 @@ absl::Status InferenceCalculatorGlImpl::LoadModel(CalculatorContext* cc) {
 absl::Status InferenceCalculatorGlImpl::LoadDelegate(CalculatorContext* cc) {
   // Configure and create the delegate.
   TfLiteGpuDelegateOptions options = TfLiteGpuDelegateOptionsDefault();
-  options.compile_options.precision_loss_allowed = 1;
+  options.compile_options.precision_loss_allowed =
+      allow_precision_loss_ ? 1 : 0;
   options.compile_options.preferred_gl_object_type =
       TFLITE_GL_OBJECT_TYPE_FASTEST;
   options.compile_options.dynamic_batch_enabled = 0;

mediapipe/calculators/tensor/inference_calculator_metal.cc

@@ -97,6 +97,7 @@ class InferenceCalculatorMetalImpl
   Packet<TfLiteModelPtr> model_packet_;
   std::unique_ptr<tflite::Interpreter> interpreter_;
   TfLiteDelegatePtr delegate_;
+  bool allow_precision_loss_ = false;
 
 #if MEDIAPIPE_TFLITE_METAL_INFERENCE
   MPPMetalHelper* gpu_helper_ = nullptr;
@@ -122,6 +123,9 @@ absl::Status InferenceCalculatorMetalImpl::UpdateContract(
 }
 
 absl::Status InferenceCalculatorMetalImpl::Open(CalculatorContext* cc) {
+  const auto& options = cc->Options<::mediapipe::InferenceCalculatorOptions>();
+  allow_precision_loss_ = options.delegate().gpu().allow_precision_loss();
+
   MP_RETURN_IF_ERROR(LoadModel(cc));
 
   gpu_helper_ = [[MPPMetalHelper alloc] initWithCalculatorContext:cc];
@@ -222,7 +226,7 @@ absl::Status InferenceCalculatorMetalImpl::LoadDelegate(CalculatorContext* cc) {
 
   // Configure and create the delegate.
   TFLGpuDelegateOptions options;
-  options.allow_precision_loss = true;
+  options.allow_precision_loss = allow_precision_loss_;
   options.wait_type = TFLGpuDelegateWaitType::TFLGpuDelegateWaitTypeDoNotWait;
   delegate_ =
       TfLiteDelegatePtr(TFLGpuDelegateCreate(&options), &TFLGpuDelegateDelete);
@@ -239,7 +243,9 @@ absl::Status InferenceCalculatorMetalImpl::LoadDelegate(CalculatorContext* cc) {
                           tensor->dims->data + tensor->dims->size};
     dims.back() = RoundUp(dims.back(), 4);
     gpu_buffers_in_.emplace_back(absl::make_unique<Tensor>(
-        Tensor::ElementType::kFloat16, Tensor::Shape{dims}));
+        allow_precision_loss_ ? Tensor::ElementType::kFloat16
+                              : Tensor::ElementType::kFloat32,
+        Tensor::Shape{dims}));
     auto buffer_view =
         gpu_buffers_in_[i]->GetMtlBufferWriteView(gpu_helper_.mtlDevice);
     RET_CHECK_EQ(TFLGpuDelegateBindMetalBufferToTensor(
@@ -261,7 +267,9 @@ absl::Status InferenceCalculatorMetalImpl::LoadDelegate(CalculatorContext* cc) {
     output_shapes_[i] = {dims};
     dims.back() = RoundUp(dims.back(), 4);
     gpu_buffers_out_.emplace_back(absl::make_unique<Tensor>(
-        Tensor::ElementType::kFloat16, Tensor::Shape{dims}));
+        allow_precision_loss_ ? Tensor::ElementType::kFloat16
+                              : Tensor::ElementType::kFloat32,
+        Tensor::Shape{dims}));
     RET_CHECK_EQ(TFLGpuDelegateBindMetalBufferToTensor(
                      delegate_.get(), output_indices[i],
                      gpu_buffers_out_[i]
@@ -271,17 +279,19 @@ absl::Status InferenceCalculatorMetalImpl::LoadDelegate(CalculatorContext* cc) {
   }
 
   // Create converter for GPU input.
-  converter_to_BPHWC4_ = [[TFLBufferConvert alloc] initWithDevice:device
+  converter_to_BPHWC4_ =
-                                                        isFloat16:true
+      [[TFLBufferConvert alloc] initWithDevice:device
-                                                  convertToPBHWC4:true];
+                                     isFloat16:allow_precision_loss_
+                               convertToPBHWC4:true];
   if (converter_to_BPHWC4_ == nil) {
     return mediapipe::InternalError(
         "Error initializating input buffer converter");
   }
   // Create converter for GPU output.
-  converter_from_BPHWC4_ = [[TFLBufferConvert alloc] initWithDevice:device
+  converter_from_BPHWC4_ =
-                                                          isFloat16:true
+      [[TFLBufferConvert alloc] initWithDevice:device
-                                                    convertToPBHWC4:false];
+                                     isFloat16:allow_precision_loss_
+                               convertToPBHWC4:false];
   if (converter_from_BPHWC4_ == nil) {
     return absl::InternalError("Error initializating output buffer converter");
   }

mediapipe/calculators/tensor/tensors_to_classification_calculator.cc

@@ -89,7 +89,8 @@ absl::Status TensorsToClassificationCalculator::Open(CalculatorContext* cc) {
     ASSIGN_OR_RETURN(string_path,
                      PathToResourceAsFile(options_.label_map_path()));
     std::string label_map_string;
-    MP_RETURN_IF_ERROR(file::GetContents(string_path, &label_map_string));
+    MP_RETURN_IF_ERROR(
+        mediapipe::GetResourceContents(string_path, &label_map_string));
 
     std::istringstream stream(label_map_string);
     std::string line;
@@ -98,6 +99,14 @@ absl::Status TensorsToClassificationCalculator::Open(CalculatorContext* cc) {
       label_map_[i++] = line;
     }
     label_map_loaded_ = true;
+  } else if (options_.has_label_map()) {
+    for (int i = 0; i < options_.label_map().entries_size(); ++i) {
+      const auto& entry = options_.label_map().entries(i);
+      RET_CHECK(!label_map_.contains(entry.id()))
+          << "Duplicate id found: " << entry.id();
+      label_map_[entry.id()] = entry.label();
+    }
+    label_map_loaded_ = true;
   }
 
   return absl::OkStatus();

mediapipe/calculators/tensor/tensors_to_classification_calculator.proto

@@ -25,6 +25,14 @@ message TensorsToClassificationCalculatorOptions {
     optional TensorsToClassificationCalculatorOptions ext = 335742638;
   }
 
+  message LabelMap {
+    message Entry {
+      optional int32 id = 1;
+      optional string label = 2;
+    }
+    repeated Entry entries = 1;
+  }
+
   // Score threshold for perserving the class.
   optional float min_score_threshold = 1;
   // Number of highest scoring labels to output.  If top_k is not positive then
@@ -32,6 +40,10 @@ message TensorsToClassificationCalculatorOptions {
   optional int32 top_k = 2;
   // Path to a label map file for getting the actual name of class ids.
   optional string label_map_path = 3;
+  // Label map. (Can be used instead of label_map_path.)
+  // NOTE: "label_map_path", if specified, takes precedence over "label_map".
+  optional LabelMap label_map = 5;
+
   // Whether the input is a single float for binary classification.
   // When true, only a single float is expected in the input tensor and the
   // label map, if provided, is expected to have exactly two labels.

mediapipe/calculators/tensor/tensors_to_classification_calculator_test.cc

@@ -115,6 +115,41 @@ TEST_F(TensorsToClassificationCalculatorTest, CorrectOutputWithLabelMapPath) {
   }
 }
 
+TEST_F(TensorsToClassificationCalculatorTest, CorrectOutputWithLabelMap) {
+  mediapipe::CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(
+    calculator: "TensorsToClassificationCalculator"
+    input_stream: "TENSORS:tensors"
+    output_stream: "CLASSIFICATIONS:classifications"
+    options {
+      [mediapipe.TensorsToClassificationCalculatorOptions.ext] {
+        label_map {
+          entries { id: 0, label: "ClassA" }
+          entries { id: 1, label: "ClassB" }
+          entries { id: 2, label: "ClassC" }
+        }
+      }
+    }
+  )pb"));
+
+  BuildGraph(&runner, {0, 0.5, 1});
+  MP_ASSERT_OK(runner.Run());
+
+  const auto& output_packets_ = runner.Outputs().Tag("CLASSIFICATIONS").packets;
+
+  EXPECT_EQ(1, output_packets_.size());
+
+  const auto& classification_list =
+      output_packets_[0].Get<ClassificationList>();
+  EXPECT_EQ(3, classification_list.classification_size());
+
+  // Verify that the label field is set.
+  for (int i = 0; i < classification_list.classification_size(); ++i) {
+    EXPECT_EQ(i, classification_list.classification(i).index());
+    EXPECT_EQ(i * 0.5, classification_list.classification(i).score());
+    ASSERT_TRUE(classification_list.classification(i).has_label());
+  }
+}
+
 TEST_F(TensorsToClassificationCalculatorTest,
        CorrectOutputWithLabelMinScoreThreshold) {
   mediapipe::CalculatorRunner runner(ParseTextProtoOrDie<Node>(R"pb(

mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator.cc

@@ -34,15 +34,28 @@ constexpr char kTensor[] = "TENSOR";
 }  // namespace
 
 // Input:
-//  Tensor of type DT_FLOAT, with values between 0-255 (SRGB or GRAY8). The
+//  Tensor of type DT_FLOAT or DT_UINT8, with values between 0-255
-//  shape can be HxWx{3,1} or simply HxW.
+//  (SRGB or GRAY8). The shape can be HxWx{3,1} or simply HxW.
 //
-//  Optionally supports a scale factor that can scale 0-1 value ranges to 0-255.
+//  For DT_FLOAT tensors, optionally supports a scale factor that can scale 0-1
+//  value ranges to 0-255.
 //
 // Output:
 //  ImageFrame containing the values of the tensor cast as uint8 (SRGB or GRAY8)
 //
 // Possible extensions: support other input ranges, maybe 4D tensors.
+//
+// Example:
+// node {
+//   calculator: "TensorToImageFrameCalculator"
+//   input_stream: "TENSOR:3d_float_tensor"
+//   output_stream: "IMAGE:image_frame"
+//   options {
+//     [mediapipe.TensorToImageFrameCalculatorOptions.ext] {
+//       scale_factor: 1.0  # set to 255.0 for [0,1] -> [0,255] scaling
+//     }
+//   }
+// }
 class TensorToImageFrameCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc);
@@ -57,8 +70,8 @@ class TensorToImageFrameCalculator : public CalculatorBase {
 REGISTER_CALCULATOR(TensorToImageFrameCalculator);
 
 absl::Status TensorToImageFrameCalculator::GetContract(CalculatorContract* cc) {
-  RET_CHECK_EQ(cc->Inputs().NumEntries(), 1)
+  RET_CHECK_EQ(cc->Outputs().NumEntries(), 1)
-      << "Only one input stream is supported.";
+      << "Only one output stream is supported.";
   RET_CHECK_EQ(cc->Inputs().NumEntries(), 1)
       << "One input stream must be provided.";
   RET_CHECK(cc->Inputs().HasTag(kTensor))
@@ -91,29 +104,44 @@ absl::Status TensorToImageFrameCalculator::Process(CalculatorContext* cc) {
       RET_CHECK_EQ(depth, 3) << "Output tensor depth must be 3 or 1.";
     }
   }
-  const int32 total_size =
+  int32 height = input_tensor.dim_size(0);
-      input_tensor.dim_size(0) * input_tensor.dim_size(1) * depth;
+  int32 width = input_tensor.dim_size(1);
-  std::unique_ptr<uint8[]> buffer(new uint8[total_size]);
+  auto format = (depth == 3 ? ImageFormat::SRGB : ImageFormat::GRAY8);
-  auto data = input_tensor.flat<float>().data();
+  const int32 total_size = height * width * depth;
-  for (int i = 0; i < total_size; ++i) {
+
-    float d = scale_factor_ * data[i];
+  ::std::unique_ptr<const ImageFrame> output;
-    if (d < 0) d = 0;
+  if (input_tensor.dtype() == tensorflow::DT_FLOAT) {
-    if (d > 255) d = 255;
+    // Allocate buffer with alignments.
-    buffer[i] = d;
+    std::unique_ptr<uint8_t[]> buffer(
+        new (std::align_val_t(EIGEN_MAX_ALIGN_BYTES)) uint8_t[total_size]);
+    auto data = input_tensor.flat<float>().data();
+    for (int i = 0; i < total_size; ++i) {
+      float d = scale_factor_ * data[i];
+      if (d < 0) d = 0;
+      if (d > 255) d = 255;
+      buffer[i] = d;
+    }
+    output = ::absl::make_unique<ImageFrame>(format, width, height,
+                                             width * depth, buffer.release());
+  } else if (input_tensor.dtype() == tensorflow::DT_UINT8) {
+    if (scale_factor_ != 1.0) {
+      return absl::InvalidArgumentError("scale_factor_ given for uint8 tensor");
+    }
+    // tf::Tensor has internally ref-counted buffer. The following code make the
+    // ImageFrame own the copied Tensor through the deleter, which increases
+    // the refcount of the buffer and allow us to use the shared buffer as the
+    // image. This allows us to create an ImageFrame object without copying
+    // buffer. const ImageFrame prevents the buffer from being modified later.
+    auto copy = new tf::Tensor(input_tensor);
+    output = ::absl::make_unique<const ImageFrame>(
+        format, width, height, width * depth, copy->flat<uint8_t>().data(),
+        [copy](uint8*) { delete copy; });
+  } else {
+    return absl::InvalidArgumentError(
+        absl::StrCat("Expected float or uint8 tensor, received ",
+                     DataTypeString(input_tensor.dtype())));
   }
 
-  ::std::unique_ptr<ImageFrame> output;
-  if (depth == 3) {
-    output = ::absl::make_unique<ImageFrame>(
-        ImageFormat::SRGB, input_tensor.dim_size(1), input_tensor.dim_size(0),
-        input_tensor.dim_size(1) * 3, buffer.release());
-  } else if (depth == 1) {
-    output = ::absl::make_unique<ImageFrame>(
-        ImageFormat::GRAY8, input_tensor.dim_size(1), input_tensor.dim_size(0),
-        input_tensor.dim_size(1), buffer.release());
-  } else {
-    return absl::InvalidArgumentError("Unrecognized image depth.");
-  }
   cc->Outputs().Tag(kImage).Add(output.release(), cc->InputTimestamp());
 
   return absl::OkStatus();

mediapipe/calculators/tensorflow/tensor_to_image_frame_calculator_test.cc

@@ -29,6 +29,7 @@ constexpr char kImage[] = "IMAGE";
 
 }  // namespace
 
+template <class TypeParam>
 class TensorToImageFrameCalculatorTest : public ::testing::Test {
  protected:
   void SetUpRunner() {
@@ -42,14 +43,20 @@ class TensorToImageFrameCalculatorTest : public ::testing::Test {
   std::unique_ptr<CalculatorRunner> runner_;
 };
 
-TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame) {
+using TensorToImageFrameCalculatorTestTypes = ::testing::Types<float, uint8_t>;
-  SetUpRunner();
+TYPED_TEST_CASE(TensorToImageFrameCalculatorTest,
+                TensorToImageFrameCalculatorTestTypes);
+
+TYPED_TEST(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame) {
+  // TYPED_TEST requires explicit "this->"
+  this->SetUpRunner();
+  auto& runner = this->runner_;
   constexpr int kWidth = 16;
   constexpr int kHeight = 8;
-  const tf::TensorShape tensor_shape(
+  const tf::TensorShape tensor_shape{kHeight, kWidth, 3};
-      std::vector<tf::int64>{kHeight, kWidth, 3});
+  auto tensor = absl::make_unique<tf::Tensor>(
-  auto tensor = absl::make_unique<tf::Tensor>(tf::DT_FLOAT, tensor_shape);
+      tf::DataTypeToEnum<TypeParam>::v(), tensor_shape);
-  auto tensor_vec = tensor->flat<float>().data();
+  auto tensor_vec = tensor->template flat<TypeParam>().data();
 
   // Writing sequence of integers as floats which we want back (as they were
   // written).
@@ -58,15 +65,16 @@ TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame) {
   }
 
   const int64 time = 1234;
-  runner_->MutableInputs()->Tag(kTensor).packets.push_back(
+  runner->MutableInputs()->Tag(kTensor).packets.push_back(
       Adopt(tensor.release()).At(Timestamp(time)));
 
-  EXPECT_TRUE(runner_->Run().ok());
+  EXPECT_TRUE(runner->Run().ok());
   const std::vector<Packet>& output_packets =
-      runner_->Outputs().Tag(kImage).packets;
+      runner->Outputs().Tag(kImage).packets;
   EXPECT_EQ(1, output_packets.size());
   EXPECT_EQ(time, output_packets[0].Timestamp().Value());
   const ImageFrame& output_image = output_packets[0].Get<ImageFrame>();
+  EXPECT_EQ(ImageFormat::SRGB, output_image.Format());
   EXPECT_EQ(kWidth, output_image.Width());
   EXPECT_EQ(kHeight, output_image.Height());
 
@@ -76,14 +84,15 @@ TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame) {
   }
 }
 
-TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrameGray) {
+TYPED_TEST(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrameGray) {
-  SetUpRunner();
+  this->SetUpRunner();
+  auto& runner = this->runner_;
   constexpr int kWidth = 16;
   constexpr int kHeight = 8;
-  const tf::TensorShape tensor_shape(
+  const tf::TensorShape tensor_shape{kHeight, kWidth, 1};
-      std::vector<tf::int64>{kHeight, kWidth, 1});
+  auto tensor = absl::make_unique<tf::Tensor>(
-  auto tensor = absl::make_unique<tf::Tensor>(tf::DT_FLOAT, tensor_shape);
+      tf::DataTypeToEnum<TypeParam>::v(), tensor_shape);
-  auto tensor_vec = tensor->flat<float>().data();
+  auto tensor_vec = tensor->template flat<TypeParam>().data();
 
   // Writing sequence of integers as floats which we want back (as they were
   // written).
@@ -92,15 +101,16 @@ TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrameGray) {
   }
 
   const int64 time = 1234;
-  runner_->MutableInputs()->Tag(kTensor).packets.push_back(
+  runner->MutableInputs()->Tag(kTensor).packets.push_back(
       Adopt(tensor.release()).At(Timestamp(time)));
 
-  EXPECT_TRUE(runner_->Run().ok());
+  EXPECT_TRUE(runner->Run().ok());
   const std::vector<Packet>& output_packets =
-      runner_->Outputs().Tag(kImage).packets;
+      runner->Outputs().Tag(kImage).packets;
   EXPECT_EQ(1, output_packets.size());
   EXPECT_EQ(time, output_packets[0].Timestamp().Value());
   const ImageFrame& output_image = output_packets[0].Get<ImageFrame>();
+  EXPECT_EQ(ImageFormat::GRAY8, output_image.Format());
   EXPECT_EQ(kWidth, output_image.Width());
   EXPECT_EQ(kHeight, output_image.Height());
 
@@ -110,13 +120,16 @@ TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrameGray) {
   }
 }
 
-TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame2DGray) {
+TYPED_TEST(TensorToImageFrameCalculatorTest,
-  SetUpRunner();
+           Converts3DTensorToImageFrame2DGray) {
+  this->SetUpRunner();
+  auto& runner = this->runner_;
   constexpr int kWidth = 16;
   constexpr int kHeight = 8;
-  const tf::TensorShape tensor_shape(std::vector<tf::int64>{kHeight, kWidth});
+  const tf::TensorShape tensor_shape{kHeight, kWidth};
-  auto tensor = absl::make_unique<tf::Tensor>(tf::DT_FLOAT, tensor_shape);
+  auto tensor = absl::make_unique<tf::Tensor>(
-  auto tensor_vec = tensor->flat<float>().data();
+      tf::DataTypeToEnum<TypeParam>::v(), tensor_shape);
+  auto tensor_vec = tensor->template flat<TypeParam>().data();
 
   // Writing sequence of integers as floats which we want back (as they were
   // written).
@@ -125,15 +138,16 @@ TEST_F(TensorToImageFrameCalculatorTest, Converts3DTensorToImageFrame2DGray) {
   }
 
   const int64 time = 1234;
-  runner_->MutableInputs()->Tag(kTensor).packets.push_back(
+  runner->MutableInputs()->Tag(kTensor).packets.push_back(
       Adopt(tensor.release()).At(Timestamp(time)));
 
-  EXPECT_TRUE(runner_->Run().ok());
+  EXPECT_TRUE(runner->Run().ok());
   const std::vector<Packet>& output_packets =
-      runner_->Outputs().Tag(kImage).packets;
+      runner->Outputs().Tag(kImage).packets;
   EXPECT_EQ(1, output_packets.size());
   EXPECT_EQ(time, output_packets[0].Timestamp().Value());
   const ImageFrame& output_image = output_packets[0].Get<ImageFrame>();
+  EXPECT_EQ(ImageFormat::GRAY8, output_image.Format());
   EXPECT_EQ(kWidth, output_image.Width());
   EXPECT_EQ(kHeight, output_image.Height());
 

mediapipe/calculators/tensorflow/tensor_to_matrix_calculator.cc

@@ -91,8 +91,6 @@ absl::Status FillTimeSeriesHeaderIfValid(const Packet& header_packet,
 // the input data when it arrives in Process(). In particular, if the header
 // states that we produce a 1xD column vector, the input tensor must also be 1xD
 //
-// This designed was discussed in http://g/speakeranalysis/4uyx7cNRwJY and
-// http://g/daredevil-project/VB26tcseUy8.
 // Example Config
 // node: {
 //   calculator: "TensorToMatrixCalculator"
@@ -158,22 +156,17 @@ absl::Status TensorToMatrixCalculator::Open(CalculatorContext* cc) {
   if (header_status.ok()) {
     if (cc->Options<TensorToMatrixCalculatorOptions>()
             .has_time_series_header_overrides()) {
-      // From design discussions with Daredevil, we only want to support single
+      // This only supports a single sample per packet for now, so we hardcode
-      // sample per packet for now, so we hardcode the sample_rate based on the
+      // the sample_rate based on the packet_rate of the REFERENCE and fail
-      // packet_rate of the REFERENCE and fail noisily if we cannot. An
+      // if we cannot.
-      // alternative would be to calculate the sample_rate from the reference
-      // sample_rate and the change in num_samples between the reference and
-      // override headers:
-      // sample_rate_output = sample_rate_reference /
-      //                      (num_samples_override / num_samples_reference)
       const TimeSeriesHeader& override_header =
           cc->Options<TensorToMatrixCalculatorOptions>()
               .time_series_header_overrides();
       input_header->MergeFrom(override_header);
-      CHECK(input_header->has_packet_rate())
+      RET_CHECK(input_header->has_packet_rate())
           << "The TimeSeriesHeader.packet_rate must be set.";
       if (!override_header.has_sample_rate()) {
-        CHECK_EQ(input_header->num_samples(), 1)
+        RET_CHECK_EQ(input_header->num_samples(), 1)
             << "Currently the time series can only output single samples.";
         input_header->set_sample_rate(input_header->packet_rate());
       }
@@ -186,20 +179,16 @@ absl::Status TensorToMatrixCalculator::Open(CalculatorContext* cc) {
 }
 
 absl::Status TensorToMatrixCalculator::Process(CalculatorContext* cc) {
-  // Daredevil requested CHECK for noisy failures rather than quieter RET_CHECK
-  // failures. These are absolute conditions of the graph for the graph to be
-  // valid, and if it is violated by any input anywhere, the graph will be
-  // invalid for all inputs. A hard CHECK will enable faster debugging by
-  // immediately exiting and more prominently displaying error messages.
-  // Do not replace with RET_CHECKs.
-
   // Verify that each reference stream packet corresponds to a tensor packet
   // otherwise the header information is invalid. If we don't have a reference
   // stream, Process() is only called when we have an input tensor and this is
   // always True.
-  CHECK(cc->Inputs().HasTag(kTensor))
+  RET_CHECK(cc->Inputs().HasTag(kTensor))
       << "Tensor stream not available at same timestamp as the reference "
          "stream.";
+  RET_CHECK(!cc->Inputs().Tag(kTensor).IsEmpty()) << "Tensor stream is empty.";
+  RET_CHECK_OK(cc->Inputs().Tag(kTensor).Value().ValidateAsType<tf::Tensor>())
+      << "Tensor stream packet does not contain a Tensor.";
 
   const tf::Tensor& input_tensor = cc->Inputs().Tag(kTensor).Get<tf::Tensor>();
   CHECK(1 == input_tensor.dims() || 2 == input_tensor.dims())
@@ -207,13 +196,12 @@ absl::Status TensorToMatrixCalculator::Process(CalculatorContext* cc) {
   const int32 length = input_tensor.dim_size(input_tensor.dims() - 1);
   const int32 width = (1 == input_tensor.dims()) ? 1 : input_tensor.dim_size(0);
   if (header_.has_num_channels()) {
-    CHECK_EQ(length, header_.num_channels())
+    RET_CHECK_EQ(length, header_.num_channels())
         << "The number of channels at runtime does not match the header.";
   }
   if (header_.has_num_samples()) {
-    CHECK_EQ(width, header_.num_samples())
+    RET_CHECK_EQ(width, header_.num_samples())
         << "The number of samples at runtime does not match the header.";
-    ;
   }
   auto output = absl::make_unique<Matrix>(width, length);
   *output =

mediapipe/calculators/tensorflow/tensorflow_inference_calculator.cc

@@ -98,388 +98,543 @@ class InferenceState {
 
 // This calculator performs inference on a trained TensorFlow model.
 //
-// A mediapipe::TensorFlowSession with a model loaded and ready for use.
+// TensorFlow Sessions can be created from checkpoint paths, frozen models, or
-// For this calculator it must include a tag_to_tensor_map.
+// the SavedModel system. See the TensorFlowSessionFrom* packet generators for
-cc->InputSidePackets().Tag("SESSION").Set<TensorFlowSession>();
+// details. Each of these methods defines a mapping between MediaPipe streams
-if (cc->InputSidePackets().HasTag("RECURRENT_INIT_TENSORS")) {
+// and TensorFlow tensors. All of this information is passed in as an
-  cc->InputSidePackets()
+// input_side_packet.
-      .Tag("RECURRENT_INIT_TENSORS")
+//
-      .Set<std::unique_ptr<std::map<std::string, tf::Tensor>>>();
+// The input and output streams are TensorFlow tensors labeled by tags. The tags
-}
+// for the streams are matched to feeds and fetchs in a TensorFlow session using
-return absl::OkStatus();
+// a named_signature.generic_signature in the ModelManifest. The
-}
+// generic_signature is used as key-value pairs between the MediaPipe tag and
+// the TensorFlow tensor. The signature_name in the options proto determines
+// which named_signature is used. The keys in the generic_signature must be
+// valid MediaPipe tags ([A-Z0-9_]*, no lowercase or special characters). All of
+// the tensors corresponding to tags in the signature for input_streams are fed
+// to the model and for output_streams the tensors are fetched from the model.
+//
+// Other calculators are used to convert data to and from tensors, this op only
+// handles the TensorFlow session and batching. Batching occurs by concatenating
+// input tensors along the 0th dimension across timestamps. If the 0th dimension
+// is not a batch dimension, this calculator will add a 0th dimension by
+// default. Setting add_batch_dim_to_tensors to false disables the dimension
+// addition. Once batch_size inputs have been provided, the batch will be run
+// and the output tensors sent out on the output streams with timestamps
+// corresponding to the input stream packets. Setting the batch_size to 1
+// completely disables batching, but is indepdent of add_batch_dim_to_tensors.
+//
+// The TensorFlowInferenceCalculator also support feeding states recurrently for
+// RNNs and LSTMs. Simply set the recurrent_tag_pair options to define the
+// recurrent tensors. Initializing the recurrent state can be handled by the
+// GraphTensorsPacketGenerator.
+//
+// The calculator updates two Counters to report timing information:
+//   --<name>-TotalTimeUsecs = Total time spent running inference (in usecs),
+//   --<name>-TotalProcessedTimestamps = # of instances processed
+//         (approximately batches processed  * batch_size),
+// where <name> is replaced with CalculatorGraphConfig::Node::name() if it
+// exists, or with TensorFlowInferenceCalculator if the name is not set. The
+// name must be set for timing information to be instance-specific in graphs
+// with multiple TensorFlowInferenceCalculators.
+//
+// Example config:
+//   packet_generator {
+//     packet_generator: "TensorFlowSessionFromSavedModelGenerator"
+//     output_side_packet: "tensorflow_session"
+//     options {
+//       [mediapipe.TensorFlowSessionFromSavedModelGeneratorOptions.ext]: {
+//         saved_model_path: "/path/to/saved/model"
+//         signature_name: "mediapipe"
+//       }
+//     }
+//   }
+//   node {
+//     calculator: "TensorFlowInferenceCalculator"
+//     input_stream: "IMAGES:image_tensors_keyed_in_signature_by_tag"
+//     input_stream: "AUDIO:audio_tensors_keyed_in_signature_by_tag"
+//     output_stream: "LABELS:softmax_tensor_keyed_in_signature_by_tag"
+//     input_side_packet: "SESSION:tensorflow_session"
+//   }
+//
+//   Where the input and output streams are treated as Packet<tf::Tensor> and
+//   the mediapipe_signature has tensor bindings between "IMAGES", "AUDIO", and
+//   "LABELS" and their respective tensors exported to /path/to/bundle. For an
+//   example of how this model was exported, see
+//   tensorflow_inference_test_graph_generator.py
+//
+// It is possible to use a GraphDef proto that was not exported by exporter (i.e
+// without MetaGraph with bindings). Such GraphDef could contain all of its
+// parameters in-lined (for example, it can be the output of freeze_graph.py).
+// To instantiate a TensorFlow model from a GraphDef file, replace the
+// packet_factory above with TensorFlowSessionFromFrozenGraphGenerator:
+//
+//   packet_generator {
+//     packet_generator: "TensorFlowSessionFromFrozenGraphGenerator"
+//     output_side_packet: "SESSION:tensorflow_session"
+//     options {
+//       [mediapipe.TensorFlowSessionFromFrozenGraphGeneratorOptions.ext]: {
+//         graph_proto_path: "[PATH]"
+//         tag_to_tensor_names {
+//           key: "JPG_STRING"
+//           value: "input:0"
+//         }
+//         tag_to_tensor_names {
+//           key: "SOFTMAX"
+//           value: "softmax:0"
+//         }
+//       }
+//     }
+//   }
+//
+// It is also possible to use a GraphDef proto and checkpoint file that have not
+// been frozen. This can be used to load graphs directly as they have been
+// written from training. However, it is more brittle and you are encouraged to
+// use a one of the more perminent formats described above. To instantiate a
+// TensorFlow model from a GraphDef file and checkpoint, replace the
+// packet_factory above with TensorFlowSessionFromModelCheckpointGenerator:
+//
+//   packet_generator {
+//     packet_generator: "TensorFlowSessionFromModelCheckpointGenerator"
+//     output_side_packet: "SESSION:tensorflow_session"
+//     options {
+//       [mediapipe.TensorFlowSessionFromModelCheckpointGeneratorOptions.ext]: {
+//         graph_proto_path: "[PATH]"
+//         model_options {
+//           checkpoint_path: "[PATH2]"
+//         }
+//         tag_to_tensor_names {
+//           key: "JPG_STRING"
+//           value: "input:0"
+//         }
+//         tag_to_tensor_names {
+//           key: "SOFTMAX"
+//           value: "softmax:0"
+//         }
+//       }
+//     }
+//   }
+class TensorFlowInferenceCalculator : public CalculatorBase {
+ public:
+  // Counters for recording timing information. The actual names have the value
+  // of CalculatorGraphConfig::Node::name() prepended.
+  static constexpr char kTotalUsecsCounterSuffix[] = "TotalTimeUsecs";
+  static constexpr char kTotalProcessedTimestampsCounterSuffix[] =
+      "TotalProcessedTimestamps";
+  static constexpr char kTotalSessionRunsTimeUsecsCounterSuffix[] =
+      "TotalSessionRunsTimeUsecs";
+  static constexpr char kTotalNumSessionRunsCounterSuffix[] =
+      "TotalNumSessionRuns";
 
-std::unique_ptr<InferenceState> CreateInferenceState(CalculatorContext* cc)
+  TensorFlowInferenceCalculator() : session_(nullptr) {
-    ABSL_EXCLUSIVE_LOCKS_REQUIRED(mutex_) {
+    clock_ = std::unique_ptr<mediapipe::Clock>(
-  std::unique_ptr<InferenceState> inference_state =
+        mediapipe::MonotonicClock::CreateSynchronizedMonotonicClock());
-      absl::make_unique<InferenceState>();
+  }
-  if (cc->InputSidePackets().HasTag("RECURRENT_INIT_TENSORS") &&
+
-      !cc->InputSidePackets().Tag("RECURRENT_INIT_TENSORS").IsEmpty()) {
+  static absl::Status GetContract(CalculatorContract* cc) {
-    std::map<std::string, tf::Tensor>* init_tensor_map;
+    const auto& options = cc->Options<TensorFlowInferenceCalculatorOptions>();
-    init_tensor_map = GetFromUniquePtr<std::map<std::string, tf::Tensor>>(
+    RET_CHECK(!cc->Inputs().GetTags().empty());
-        cc->InputSidePackets().Tag("RECURRENT_INIT_TENSORS"));
+    for (const std::string& tag : cc->Inputs().GetTags()) {
-    for (const auto& p : *init_tensor_map) {
+      // The tensorflow::Tensor with the tag equal to the graph node. May
-      inference_state->input_tensor_batches_[p.first].emplace_back(p.second);
+      // have a TimeSeriesHeader if all present TimeSeriesHeaders match.
+      if (!options.batched_input()) {
+        cc->Inputs().Tag(tag).Set<tf::Tensor>();
+      } else {
+        cc->Inputs().Tag(tag).Set<std::vector<mediapipe::Packet>>();
+      }
     }
-  }
+    RET_CHECK(!cc->Outputs().GetTags().empty());
-  return inference_state;
+    for (const std::string& tag : cc->Outputs().GetTags()) {
-}
+      // The tensorflow::Tensor with tag equal to the graph node to
-
+      // output.  Any TimeSeriesHeader from the inputs will be forwarded
-absl::Status Open(CalculatorContext* cc) override {
+      // with channels set to 0.
-  options_ = cc->Options<TensorFlowInferenceCalculatorOptions>();
+      cc->Outputs().Tag(tag).Set<tf::Tensor>();
-
-  RET_CHECK(cc->InputSidePackets().HasTag("SESSION"));
-  session_ = cc->InputSidePackets()
-                 .Tag("SESSION")
-                 .Get<TensorFlowSession>()
-                 .session.get();
-  tag_to_tensor_map_ = cc->InputSidePackets()
-                           .Tag("SESSION")
-                           .Get<TensorFlowSession>()
-                           .tag_to_tensor_map;
-
-  // Validate and store the recurrent tags
-  RET_CHECK(options_.has_batch_size());
-  RET_CHECK(options_.batch_size() == 1 || options_.recurrent_tag_pair().empty())
-      << "To use recurrent_tag_pairs, batch_size must be 1.";
-  for (const auto& tag_pair : options_.recurrent_tag_pair()) {
-    const std::vector<std::string> tags = absl::StrSplit(tag_pair, ':');
-    RET_CHECK_EQ(tags.size(), 2)
-        << "recurrent_tag_pair must be a colon "
-           "separated std::string with two components: "
-        << tag_pair;
-    RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tags[0]))
-        << "Can't find tag '" << tags[0] << "' in signature "
-        << options_.signature_name();
-    RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tags[1]))
-        << "Can't find tag '" << tags[1] << "' in signature "
-        << options_.signature_name();
-    recurrent_feed_tags_.insert(tags[0]);
-    recurrent_fetch_tags_to_feed_tags_[tags[1]] = tags[0];
-  }
-
-  // Check that all tags are present in this signature bound to tensors.
-  for (const std::string& tag : cc->Inputs().GetTags()) {
-    RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tag))
-        << "Can't find tag '" << tag << "' in signature "
-        << options_.signature_name();
-  }
-  for (const std::string& tag : cc->Outputs().GetTags()) {
-    RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tag))
-        << "Can't find tag '" << tag << "' in signature "
-        << options_.signature_name();
-  }
-
-  {
-    absl::WriterMutexLock l(&mutex_);
-    inference_state_ = std::unique_ptr<InferenceState>();
-  }
-
-  if (options_.batch_size() == 1 || options_.batched_input()) {
-    cc->SetOffset(0);
-  }
-
-  return absl::OkStatus();
-}
-
-// Adds a batch dimension to the input tensor if specified in the calculator
-// options.
-absl::Status AddBatchDimension(tf::Tensor* input_tensor) {
-  if (options_.add_batch_dim_to_tensors()) {
-    tf::TensorShape new_shape(input_tensor->shape());
-    new_shape.InsertDim(0, 1);
-    RET_CHECK(input_tensor->CopyFrom(*input_tensor, new_shape))
-        << "Could not add 0th dimension to tensor without changing its shape."
-        << " Current shape: " << input_tensor->shape().DebugString();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status AggregateTensorPacket(
-    const std::string& tag_name, const Packet& packet,
-    std::map<Timestamp, std::map<std::string, tf::Tensor>>*
-        input_tensors_by_tag_by_timestamp,
-    InferenceState* inference_state) ABSL_EXCLUSIVE_LOCKS_REQUIRED(mutex_) {
-  tf::Tensor input_tensor(packet.Get<tf::Tensor>());
-  RET_CHECK_OK(AddBatchDimension(&input_tensor));
-  if (mediapipe::ContainsKey(recurrent_feed_tags_, tag_name)) {
-    // If we receive an input on a recurrent tag, override the state.
-    // It's OK to override the global state because there is just one
-    // input stream allowed for recurrent tensors.
-    inference_state_->input_tensor_batches_[tag_name].clear();
-  }
-  (*input_tensors_by_tag_by_timestamp)[packet.Timestamp()].insert(
-      std::make_pair(tag_name, input_tensor));
-  return absl::OkStatus();
-}
-
-// Removes the batch dimension of the output tensor if specified in the
-// calculator options.
-absl::Status RemoveBatchDimension(tf::Tensor* output_tensor) {
-  if (options_.add_batch_dim_to_tensors()) {
-    tf::TensorShape new_shape(output_tensor->shape());
-    new_shape.RemoveDim(0);
-    RET_CHECK(output_tensor->CopyFrom(*output_tensor, new_shape))
-        << "Could not remove 0th dimension from tensor without changing its "
-        << "shape. Current shape: " << output_tensor->shape().DebugString()
-        << " (The expected first dimension is 1 for a batch element.)";
-  }
-  return absl::OkStatus();
-}
-
-absl::Status Process(CalculatorContext* cc) override {
-  std::unique_ptr<InferenceState> inference_state_to_process;
-  {
-    absl::WriterMutexLock l(&mutex_);
-    if (inference_state_ == nullptr) {
-      inference_state_ = CreateInferenceState(cc);
     }
-    std::map<Timestamp, std::map<std::string, tf::Tensor>>
+    // A mediapipe::TensorFlowSession with a model loaded and ready for use.
-        input_tensors_by_tag_by_timestamp;
+    // For this calculator it must include a tag_to_tensor_map.
-    for (const std::string& tag_as_node_name : cc->Inputs().GetTags()) {
+    cc->InputSidePackets().Tag("SESSION").Set<TensorFlowSession>();
-      if (cc->Inputs().Tag(tag_as_node_name).IsEmpty()) {
+    if (cc->InputSidePackets().HasTag("RECURRENT_INIT_TENSORS")) {
-        // Recurrent tensors can be empty.
+      cc->InputSidePackets()
-        if (!mediapipe::ContainsKey(recurrent_feed_tags_, tag_as_node_name)) {
+          .Tag("RECURRENT_INIT_TENSORS")
-          if (options_.skip_on_missing_features()) {
+          .Set<std::unique_ptr<std::map<std::string, tf::Tensor>>>();
-            return absl::OkStatus();
+    }
-          } else {
+    return absl::OkStatus();
-            return absl::InvalidArgumentError(absl::StrCat(
+  }
-                "Tag ", tag_as_node_name,
+
-                " not present at timestamp: ", cc->InputTimestamp().Value()));
+  std::unique_ptr<InferenceState> CreateInferenceState(CalculatorContext* cc)
+      ABSL_EXCLUSIVE_LOCKS_REQUIRED(mutex_) {
+    std::unique_ptr<InferenceState> inference_state =
+        absl::make_unique<InferenceState>();
+    if (cc->InputSidePackets().HasTag("RECURRENT_INIT_TENSORS") &&
+        !cc->InputSidePackets().Tag("RECURRENT_INIT_TENSORS").IsEmpty()) {
+      std::map<std::string, tf::Tensor>* init_tensor_map;
+      init_tensor_map = GetFromUniquePtr<std::map<std::string, tf::Tensor>>(
+          cc->InputSidePackets().Tag("RECURRENT_INIT_TENSORS"));
+      for (const auto& p : *init_tensor_map) {
+        inference_state->input_tensor_batches_[p.first].emplace_back(p.second);
+      }
+    }
+    return inference_state;
+  }
+
+  absl::Status Open(CalculatorContext* cc) override {
+    options_ = cc->Options<TensorFlowInferenceCalculatorOptions>();
+
+    RET_CHECK(cc->InputSidePackets().HasTag("SESSION"));
+    session_ = cc->InputSidePackets()
+                   .Tag("SESSION")
+                   .Get<TensorFlowSession>()
+                   .session.get();
+    tag_to_tensor_map_ = cc->InputSidePackets()
+                             .Tag("SESSION")
+                             .Get<TensorFlowSession>()
+                             .tag_to_tensor_map;
+
+    // Validate and store the recurrent tags
+    RET_CHECK(options_.has_batch_size());
+    RET_CHECK(options_.batch_size() == 1 ||
+              options_.recurrent_tag_pair().empty())
+        << "To use recurrent_tag_pairs, batch_size must be 1.";
+    for (const auto& tag_pair : options_.recurrent_tag_pair()) {
+      const std::vector<std::string> tags = absl::StrSplit(tag_pair, ':');
+      RET_CHECK_EQ(tags.size(), 2)
+          << "recurrent_tag_pair must be a colon "
+             "separated std::string with two components: "
+          << tag_pair;
+      RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tags[0]))
+          << "Can't find tag '" << tags[0] << "' in signature "
+          << options_.signature_name();
+      RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tags[1]))
+          << "Can't find tag '" << tags[1] << "' in signature "
+          << options_.signature_name();
+      recurrent_feed_tags_.insert(tags[0]);
+      recurrent_fetch_tags_to_feed_tags_[tags[1]] = tags[0];
+    }
+
+    // Check that all tags are present in this signature bound to tensors.
+    for (const std::string& tag : cc->Inputs().GetTags()) {
+      RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tag))
+          << "Can't find tag '" << tag << "' in signature "
+          << options_.signature_name();
+    }
+    for (const std::string& tag : cc->Outputs().GetTags()) {
+      RET_CHECK(mediapipe::ContainsKey(tag_to_tensor_map_, tag))
+          << "Can't find tag '" << tag << "' in signature "
+          << options_.signature_name();
+    }
+
+    {
+      absl::WriterMutexLock l(&mutex_);
+      inference_state_ = std::unique_ptr<InferenceState>();
+    }
+
+    if (options_.batch_size() == 1 || options_.batched_input()) {
+      cc->SetOffset(0);
+    }
+
+    return absl::OkStatus();
+  }
+
+  // Adds a batch dimension to the input tensor if specified in the calculator
+  // options.
+  absl::Status AddBatchDimension(tf::Tensor* input_tensor) {
+    if (options_.add_batch_dim_to_tensors()) {
+      tf::TensorShape new_shape(input_tensor->shape());
+      new_shape.InsertDim(0, 1);
+      RET_CHECK(input_tensor->CopyFrom(*input_tensor, new_shape))
+          << "Could not add 0th dimension to tensor without changing its shape."
+          << " Current shape: " << input_tensor->shape().DebugString();
+    }
+    return absl::OkStatus();
+  }
+
+  absl::Status AggregateTensorPacket(
+      const std::string& tag_name, const Packet& packet,
+      std::map<Timestamp, std::map<std::string, tf::Tensor>>*
+          input_tensors_by_tag_by_timestamp,
+      InferenceState* inference_state) ABSL_EXCLUSIVE_LOCKS_REQUIRED(mutex_) {
+    tf::Tensor input_tensor(packet.Get<tf::Tensor>());
+    RET_CHECK_OK(AddBatchDimension(&input_tensor));
+    if (mediapipe::ContainsKey(recurrent_feed_tags_, tag_name)) {
+      // If we receive an input on a recurrent tag, override the state.
+      // It's OK to override the global state because there is just one
+      // input stream allowed for recurrent tensors.
+      inference_state_->input_tensor_batches_[tag_name].clear();
+    }
+    (*input_tensors_by_tag_by_timestamp)[packet.Timestamp()].insert(
+        std::make_pair(tag_name, input_tensor));
+    return absl::OkStatus();
+  }
+
+  // Removes the batch dimension of the output tensor if specified in the
+  // calculator options.
+  absl::Status RemoveBatchDimension(tf::Tensor* output_tensor) {
+    if (options_.add_batch_dim_to_tensors()) {
+      tf::TensorShape new_shape(output_tensor->shape());
+      new_shape.RemoveDim(0);
+      RET_CHECK(output_tensor->CopyFrom(*output_tensor, new_shape))
+          << "Could not remove 0th dimension from tensor without changing its "
+          << "shape. Current shape: " << output_tensor->shape().DebugString()
+          << " (The expected first dimension is 1 for a batch element.)";
+    }
+    return absl::OkStatus();
+  }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    std::unique_ptr<InferenceState> inference_state_to_process;
+    {
+      absl::WriterMutexLock l(&mutex_);
+      if (inference_state_ == nullptr) {
+        inference_state_ = CreateInferenceState(cc);
+      }
+      std::map<Timestamp, std::map<std::string, tf::Tensor>>
+          input_tensors_by_tag_by_timestamp;
+      for (const std::string& tag_as_node_name : cc->Inputs().GetTags()) {
+        if (cc->Inputs().Tag(tag_as_node_name).IsEmpty()) {
+          // Recurrent tensors can be empty.
+          if (!mediapipe::ContainsKey(recurrent_feed_tags_, tag_as_node_name)) {
+            if (options_.skip_on_missing_features()) {
+              return absl::OkStatus();
+            } else {
+              return absl::InvalidArgumentError(absl::StrCat(
+                  "Tag ", tag_as_node_name,
+                  " not present at timestamp: ", cc->InputTimestamp().Value()));
+            }
           }
+        } else if (options_.batched_input()) {
+          const auto& tensor_packets =
+              cc->Inputs().Tag(tag_as_node_name).Get<std::vector<Packet>>();
+          if (tensor_packets.size() > options_.batch_size()) {
+            return absl::InvalidArgumentError(absl::StrCat(
+                "Batch for tag ", tag_as_node_name,
+                " has more packets than batch capacity. batch_size: ",
+                options_.batch_size(), " packets: ", tensor_packets.size()));
+          }
+          for (const auto& packet : tensor_packets) {
+            RET_CHECK_OK(AggregateTensorPacket(
+                tag_as_node_name, packet, &input_tensors_by_tag_by_timestamp,
+                inference_state_.get()));
+          }
+        } else {
+          RET_CHECK_OK(AggregateTensorPacket(
+              tag_as_node_name, cc->Inputs().Tag(tag_as_node_name).Value(),
+              &input_tensors_by_tag_by_timestamp, inference_state_.get()));
         }
-      } else if (options_.batched_input()) {
+      }
-        const auto& tensor_packets =
+      for (const auto& timestamp_and_input_tensors_by_tag :
-            cc->Inputs().Tag(tag_as_node_name).Get<std::vector<Packet>>();
+           input_tensors_by_tag_by_timestamp) {
-        if (tensor_packets.size() > options_.batch_size()) {
+        inference_state_->batch_timestamps_.emplace_back(
-          return absl::InvalidArgumentError(absl::StrCat(
+            timestamp_and_input_tensors_by_tag.first);
-              "Batch for tag ", tag_as_node_name,
+        for (const auto& input_tensor_and_tag :
-              " has more packets than batch capacity. batch_size: ",
+             timestamp_and_input_tensors_by_tag.second) {
-              options_.batch_size(), " packets: ", tensor_packets.size()));
+          inference_state_->input_tensor_batches_[input_tensor_and_tag.first]
+              .emplace_back(input_tensor_and_tag.second);
         }
-        for (const auto& packet : tensor_packets) {
+      }
-          RET_CHECK_OK(AggregateTensorPacket(tag_as_node_name, packet,
+      if (inference_state_->batch_timestamps_.size() == options_.batch_size() ||
-                                             &input_tensors_by_tag_by_timestamp,
+          options_.batched_input()) {
-                                             inference_state_.get()));
+        inference_state_to_process = std::move(inference_state_);
+        inference_state_ = std::unique_ptr<InferenceState>();
+      }
+    }
+
+    if (inference_state_to_process) {
+      MP_RETURN_IF_ERROR(
+          OutputBatch(cc, std::move(inference_state_to_process)));
+    }
+
+    return absl::OkStatus();
+  }
+
+  absl::Status Close(CalculatorContext* cc) override {
+    std::unique_ptr<InferenceState> inference_state_to_process = nullptr;
+    {
+      absl::WriterMutexLock l(&mutex_);
+      if (cc->GraphStatus().ok() && inference_state_ != nullptr &&
+          !inference_state_->batch_timestamps_.empty()) {
+        inference_state_to_process = std::move(inference_state_);
+        inference_state_ = std::unique_ptr<InferenceState>();
+      }
+    }
+    if (inference_state_to_process) {
+      MP_RETURN_IF_ERROR(
+          OutputBatch(cc, std::move(inference_state_to_process)));
+    }
+    return absl::OkStatus();
+  }
+
+  // When a batch of input tensors is ready to be run, runs TensorFlow and
+  // outputs the output tensors. The output tensors have timestamps matching
+  // the input tensor that formed that batch element. Any requested
+  // batch_dimension is added and removed. This code takes advantage of the fact
+  // that copying a tensor shares the same reference-counted, heap allocated
+  // memory buffer. Therefore, copies are cheap and should not cause the memory
+  // buffer to fall out of scope. In contrast, concat is only used where
+  // necessary.
+  absl::Status OutputBatch(CalculatorContext* cc,
+                           std::unique_ptr<InferenceState> inference_state) {
+    const int64 start_time = absl::ToUnixMicros(clock_->TimeNow());
+    std::vector<std::pair<mediapipe::ProtoString, tf::Tensor>> input_tensors;
+
+    for (auto& keyed_tensors : inference_state->input_tensor_batches_) {
+      if (options_.batch_size() == 1) {
+        // Short circuit to avoid the cost of deep copying tensors in concat.
+        if (!keyed_tensors.second.empty()) {
+          input_tensors.emplace_back(tag_to_tensor_map_[keyed_tensors.first],
+                                     keyed_tensors.second[0]);
+        } else {
+          // The input buffer can be empty for recurrent tensors.
+          RET_CHECK(
+              mediapipe::ContainsKey(recurrent_feed_tags_, keyed_tensors.first))
+              << "A non-recurrent tensor does not have an input: "
+              << keyed_tensors.first;
         }
       } else {
-        RET_CHECK_OK(AggregateTensorPacket(
+        // Pad by replicating the first tens  or, then ignore the values.
-            tag_as_node_name, cc->Inputs().Tag(tag_as_node_name).Value(),
+        keyed_tensors.second.resize(options_.batch_size());
-            &input_tensors_by_tag_by_timestamp, inference_state_.get()));
+        std::fill(keyed_tensors.second.begin() +
-      }
+                      inference_state->batch_timestamps_.size(),
-    }
+                  keyed_tensors.second.end(), keyed_tensors.second[0]);
-    for (const auto& timestamp_and_input_tensors_by_tag :
+        tf::Tensor concated;
-         input_tensors_by_tag_by_timestamp) {
+        const tf::Status concat_status =
-      inference_state_->batch_timestamps_.emplace_back(
+            tf::tensor::Concat(keyed_tensors.second, &concated);
-          timestamp_and_input_tensors_by_tag.first);
+        CHECK(concat_status.ok()) << concat_status.ToString();
-      for (const auto& input_tensor_and_tag :
-           timestamp_and_input_tensors_by_tag.second) {
-        inference_state_->input_tensor_batches_[input_tensor_and_tag.first]
-            .emplace_back(input_tensor_and_tag.second);
-      }
-    }
-    if (inference_state_->batch_timestamps_.size() == options_.batch_size() ||
-        options_.batched_input()) {
-      inference_state_to_process = std::move(inference_state_);
-      inference_state_ = std::unique_ptr<InferenceState>();
-    }
-  }
-
-  if (inference_state_to_process) {
-    MP_RETURN_IF_ERROR(OutputBatch(cc, std::move(inference_state_to_process)));
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status Close(CalculatorContext* cc) override {
-  std::unique_ptr<InferenceState> inference_state_to_process = nullptr;
-  {
-    absl::WriterMutexLock l(&mutex_);
-    if (cc->GraphStatus().ok() && inference_state_ != nullptr &&
-        !inference_state_->batch_timestamps_.empty()) {
-      inference_state_to_process = std::move(inference_state_);
-      inference_state_ = std::unique_ptr<InferenceState>();
-    }
-  }
-  if (inference_state_to_process) {
-    MP_RETURN_IF_ERROR(OutputBatch(cc, std::move(inference_state_to_process)));
-  }
-  return absl::OkStatus();
-}
-
-// When a batch of input tensors is ready to be run, runs TensorFlow and
-// outputs the output tensors. The output tensors have timestamps matching
-// the input tensor that formed that batch element. Any requested
-// batch_dimension is added and removed. This code takes advantage of the fact
-// that copying a tensor shares the same reference-counted, heap allocated
-// memory buffer. Therefore, copies are cheap and should not cause the memory
-// buffer to fall out of scope. In contrast, concat is only used where
-// necessary.
-absl::Status OutputBatch(CalculatorContext* cc,
-                         std::unique_ptr<InferenceState> inference_state) {
-  const int64 start_time = absl::ToUnixMicros(clock_->TimeNow());
-  std::vector<std::pair<mediapipe::ProtoString, tf::Tensor>> input_tensors;
-
-  for (auto& keyed_tensors : inference_state->input_tensor_batches_) {
-    if (options_.batch_size() == 1) {
-      // Short circuit to avoid the cost of deep copying tensors in concat.
-      if (!keyed_tensors.second.empty()) {
         input_tensors.emplace_back(tag_to_tensor_map_[keyed_tensors.first],
-                                   keyed_tensors.second[0]);
+                                   concated);
-      } else {
-        // The input buffer can be empty for recurrent tensors.
-        RET_CHECK(
-            mediapipe::ContainsKey(recurrent_feed_tags_, keyed_tensors.first))
-            << "A non-recurrent tensor does not have an input: "
-            << keyed_tensors.first;
       }
-    } else {
-      // Pad by replicating the first tens  or, then ignore the values.
-      keyed_tensors.second.resize(options_.batch_size());
-      std::fill(keyed_tensors.second.begin() +
-                    inference_state->batch_timestamps_.size(),
-                keyed_tensors.second.end(), keyed_tensors.second[0]);
-      tf::Tensor concated;
-      const tf::Status concat_status =
-          tf::tensor::Concat(keyed_tensors.second, &concated);
-      CHECK(concat_status.ok()) << concat_status.ToString();
-      input_tensors.emplace_back(tag_to_tensor_map_[keyed_tensors.first],
-                                 concated);
     }
-  }
+    inference_state->input_tensor_batches_.clear();
-  inference_state->input_tensor_batches_.clear();
+    std::vector<mediapipe::ProtoString> output_tensor_names;
-  std::vector<mediapipe::ProtoString> output_tensor_names;
+    std::vector<std::string> output_name_in_signature;
-  std::vector<std::string> output_name_in_signature;
+    for (const std::string& tag : cc->Outputs().GetTags()) {
-  for (const std::string& tag : cc->Outputs().GetTags()) {
+      output_tensor_names.emplace_back(tag_to_tensor_map_[tag]);
-    output_tensor_names.emplace_back(tag_to_tensor_map_[tag]);
+      output_name_in_signature.emplace_back(tag);
-    output_name_in_signature.emplace_back(tag);
-  }
-  for (const auto& tag_pair : recurrent_fetch_tags_to_feed_tags_) {
-    // Ensure that we always fetch the recurrent state tensors.
-    if (std::find(output_name_in_signature.begin(),
-                  output_name_in_signature.end(),
-                  tag_pair.first) == output_name_in_signature.end()) {
-      output_tensor_names.emplace_back(tag_to_tensor_map_[tag_pair.first]);
-      output_name_in_signature.emplace_back(tag_pair.first);
     }
-  }
+    for (const auto& tag_pair : recurrent_fetch_tags_to_feed_tags_) {
-  std::vector<tf::Tensor> outputs;
+      // Ensure that we always fetch the recurrent state tensors.
+      if (std::find(output_name_in_signature.begin(),
+                    output_name_in_signature.end(),
+                    tag_pair.first) == output_name_in_signature.end()) {
+        output_tensor_names.emplace_back(tag_to_tensor_map_[tag_pair.first]);
+        output_name_in_signature.emplace_back(tag_pair.first);
+      }
+    }
+    std::vector<tf::Tensor> outputs;
 
-  SimpleSemaphore* session_run_throttle = nullptr;
+    SimpleSemaphore* session_run_throttle = nullptr;
-  if (options_.max_concurrent_session_runs() > 0) {
+    if (options_.max_concurrent_session_runs() > 0) {
-    session_run_throttle =
+      session_run_throttle =
-        get_session_run_throttle(options_.max_concurrent_session_runs());
+          get_session_run_throttle(options_.max_concurrent_session_runs());
-    session_run_throttle->Acquire(1);
+      session_run_throttle->Acquire(1);
-  }
+    }
-  const int64 run_start_time = absl::ToUnixMicros(clock_->TimeNow());
+    const int64 run_start_time = absl::ToUnixMicros(clock_->TimeNow());
-  tf::Status tf_status;
+    tf::Status tf_status;
-  {
+    {
 #if !defined(MEDIAPIPE_MOBILE) && !defined(__APPLE__)
-    tensorflow::profiler::TraceMe trace(absl::string_view(cc->NodeName()));
+      tensorflow::profiler::TraceMe trace(absl::string_view(cc->NodeName()));
 #endif
-    tf_status = session_->Run(input_tensors, output_tensor_names,
+      tf_status = session_->Run(input_tensors, output_tensor_names,
-                              {} /* target_node_names */, &outputs);
+                                {} /* target_node_names */, &outputs);
-  }
+    }
 
-  if (session_run_throttle != nullptr) {
+    if (session_run_throttle != nullptr) {
-    session_run_throttle->Release(1);
+      session_run_throttle->Release(1);
-  }
+    }
 
-  // RET_CHECK on the tf::Status object itself in order to print an
+    // RET_CHECK on the tf::Status object itself in order to print an
-  // informative error message.
+    // informative error message.
-  RET_CHECK(tf_status.ok()) << "Run failed: " << tf_status.ToString();
+    RET_CHECK(tf_status.ok()) << "Run failed: " << tf_status.ToString();
 
-  const int64 run_end_time = absl::ToUnixMicros(clock_->TimeNow());
+    const int64 run_end_time = absl::ToUnixMicros(clock_->TimeNow());
-  cc->GetCounter(kTotalSessionRunsTimeUsecsCounterSuffix)
+    cc->GetCounter(kTotalSessionRunsTimeUsecsCounterSuffix)
-      ->IncrementBy(run_end_time - run_start_time);
+        ->IncrementBy(run_end_time - run_start_time);
-  cc->GetCounter(kTotalNumSessionRunsCounterSuffix)->Increment();
+    cc->GetCounter(kTotalNumSessionRunsCounterSuffix)->Increment();
 
-  // Feed back the recurrent state.
+    // Feed back the recurrent state.
-  for (const auto& tag_pair : recurrent_fetch_tags_to_feed_tags_) {
+    for (const auto& tag_pair : recurrent_fetch_tags_to_feed_tags_) {
-    int pos = std::find(output_name_in_signature.begin(),
+      int pos = std::find(output_name_in_signature.begin(),
-                        output_name_in_signature.end(), tag_pair.first) -
+                          output_name_in_signature.end(), tag_pair.first) -
-              output_name_in_signature.begin();
+                output_name_in_signature.begin();
-    inference_state->input_tensor_batches_[tag_pair.second].emplace_back(
+      inference_state->input_tensor_batches_[tag_pair.second].emplace_back(
-        outputs[pos]);
+          outputs[pos]);
-  }
+    }
 
-  absl::WriterMutexLock l(&mutex_);
+    absl::WriterMutexLock l(&mutex_);
-  // Set that we want to split on each index of the 0th dimension.
+    // Set that we want to split on each index of the 0th dimension.
-  std::vector<tf::int64> split_vector(options_.batch_size(), 1);
+    std::vector<tf::int64> split_vector(options_.batch_size(), 1);
-  for (int i = 0; i < output_tensor_names.size(); ++i) {
+    for (int i = 0; i < output_tensor_names.size(); ++i) {
-    if (options_.batch_size() == 1) {
+      if (options_.batch_size() == 1) {
-      if (cc->Outputs().HasTag(output_name_in_signature[i])) {
+        if (cc->Outputs().HasTag(output_name_in_signature[i])) {
-        tf::Tensor output_tensor(outputs[i]);
+          tf::Tensor output_tensor(outputs[i]);
-        RET_CHECK_OK(RemoveBatchDimension(&output_tensor));
+          RET_CHECK_OK(RemoveBatchDimension(&output_tensor));
-        cc->Outputs()
+          cc->Outputs()
-            .Tag(output_name_in_signature[i])
+              .Tag(output_name_in_signature[i])
-            .Add(new tf::Tensor(output_tensor),
+              .Add(new tf::Tensor(output_tensor),
-                 inference_state->batch_timestamps_[0]);
+                   inference_state->batch_timestamps_[0]);
-      }
+        }
-    } else {
+      } else {
-      std::vector<tf::Tensor> split_tensors;
+        std::vector<tf::Tensor> split_tensors;
-      const tf::Status split_status =
+        const tf::Status split_status =
-          tf::tensor::Split(outputs[i], split_vector, &split_tensors);
+            tf::tensor::Split(outputs[i], split_vector, &split_tensors);
-      CHECK(split_status.ok()) << split_status.ToString();
+        CHECK(split_status.ok()) << split_status.ToString();
-      // Loop over timestamps so that we don't copy the padding.
+        // Loop over timestamps so that we don't copy the padding.
-      for (int j = 0; j < inference_state->batch_timestamps_.size(); ++j) {
+        for (int j = 0; j < inference_state->batch_timestamps_.size(); ++j) {
-        tf::Tensor output_tensor(split_tensors[j]);
+          tf::Tensor output_tensor(split_tensors[j]);
-        RET_CHECK_OK(RemoveBatchDimension(&output_tensor));
+          RET_CHECK_OK(RemoveBatchDimension(&output_tensor));
-        cc->Outputs()
+          cc->Outputs()
-            .Tag(output_name_in_signature[i])
+              .Tag(output_name_in_signature[i])
-            .Add(new tf::Tensor(output_tensor),
+              .Add(new tf::Tensor(output_tensor),
-                 inference_state->batch_timestamps_[j]);
+                   inference_state->batch_timestamps_[j]);
+        }
       }
     }
+
+    // Get end time and report.
+    const int64 end_time = absl::ToUnixMicros(clock_->TimeNow());
+    cc->GetCounter(kTotalUsecsCounterSuffix)
+        ->IncrementBy(end_time - start_time);
+    cc->GetCounter(kTotalProcessedTimestampsCounterSuffix)
+        ->IncrementBy(inference_state->batch_timestamps_.size());
+
+    // Make sure we hold on to the recursive state.
+    if (!options_.recurrent_tag_pair().empty()) {
+      inference_state_ = std::move(inference_state);
+      inference_state_->batch_timestamps_.clear();
+    }
+
+    return absl::OkStatus();
   }
 
-  // Get end time and report.
+ private:
-  const int64 end_time = absl::ToUnixMicros(clock_->TimeNow());
+  // The Session object is provided by a packet factory and is owned by the
-  cc->GetCounter(kTotalUsecsCounterSuffix)->IncrementBy(end_time - start_time);
+  // MediaPipe framework. Individual calls are thread-safe, but session state
-  cc->GetCounter(kTotalProcessedTimestampsCounterSuffix)
+  // may be shared across threads.
-      ->IncrementBy(inference_state->batch_timestamps_.size());
+  tf::Session* session_;
 
-  // Make sure we hold on to the recursive state.
+  // A mapping between stream tags and the tensor names they are bound to.
-  if (!options_.recurrent_tag_pair().empty()) {
+  std::map<std::string, std::string> tag_to_tensor_map_;
-    inference_state_ = std::move(inference_state);
+
-    inference_state_->batch_timestamps_.clear();
+  absl::Mutex mutex_;
+  std::unique_ptr<InferenceState> inference_state_ ABSL_GUARDED_BY(mutex_);
+
+  // The options for the calculator.
+  TensorFlowInferenceCalculatorOptions options_;
+
+  // Store the feed and fetch tags for feed/fetch recurrent networks.
+  std::set<std::string> recurrent_feed_tags_;
+  std::map<std::string, std::string> recurrent_fetch_tags_to_feed_tags_;
+
+  // Clock used to measure the computation time in OutputBatch().
+  std::unique_ptr<mediapipe::Clock> clock_;
+
+  // The static singleton semaphore to throttle concurrent session runs.
+  static SimpleSemaphore* get_session_run_throttle(
+      int32 max_concurrent_session_runs) {
+    static SimpleSemaphore* session_run_throttle =
+        new SimpleSemaphore(max_concurrent_session_runs);
+    return session_run_throttle;
   }
-
+};
-  return absl::OkStatus();
-}
-
-private:
-// The Session object is provided by a packet factory and is owned by the
-// MediaPipe framework. Individual calls are thread-safe, but session state may
-// be shared across threads.
-tf::Session* session_;
-
-// A mapping between stream tags and the tensor names they are bound to.
-std::map<std::string, std::string> tag_to_tensor_map_;
-
-absl::Mutex mutex_;
-std::unique_ptr<InferenceState> inference_state_ ABSL_GUARDED_BY(mutex_);
-
-// The options for the calculator.
-TensorFlowInferenceCalculatorOptions options_;
-
-// Store the feed and fetch tags for feed/fetch recurrent networks.
-std::set<std::string> recurrent_feed_tags_;
-std::map<std::string, std::string> recurrent_fetch_tags_to_feed_tags_;
-
-// Clock used to measure the computation time in OutputBatch().
-std::unique_ptr<mediapipe::Clock> clock_;
-
-// The static singleton semaphore to throttle concurrent session runs.
-static SimpleSemaphore* get_session_run_throttle(
-    int32 max_concurrent_session_runs) {
-  static SimpleSemaphore* session_run_throttle =
-      new SimpleSemaphore(max_concurrent_session_runs);
-  return session_run_throttle;
-}
-}
-;
 REGISTER_CALCULATOR(TensorFlowInferenceCalculator);
 
 constexpr char TensorFlowInferenceCalculator::kTotalUsecsCounterSuffix[];

mediapipe/calculators/tensorflow/tensorflow_session_from_saved_model_calculator.cc

@@ -80,6 +80,7 @@ const std::string MaybeConvertSignatureToTag(
 // which in turn contains a TensorFlow Session ready for execution and a map
 // between tags and tensor names.
 //
+//
 // Example usage:
 // node {
 //   calculator: "TensorFlowSessionFromSavedModelCalculator"

mediapipe/calculators/tensorflow/unpack_media_sequence_calculator.cc

@@ -217,38 +217,41 @@ class UnpackMediaSequenceCalculator : public CalculatorBase {
             first_timestamp_seen_ = recent_timestamp;
           }
         }
-        if (recent_timestamp > last_timestamp_seen) {
+        if (recent_timestamp > last_timestamp_seen &&
+            recent_timestamp < Timestamp::PostStream().Value()) {
           last_timestamp_key_ = map_kv.first;
           last_timestamp_seen = recent_timestamp;
         }
       }
     }
     if (!timestamps_.empty()) {
-      RET_CHECK(!last_timestamp_key_.empty())
+      for (const auto& kv : timestamps_) {
-          << "Something went wrong because the timestamp key is unset. "
+        if (!kv.second.empty() &&
-             "Example: "
+            kv.second[0] < Timestamp::PostStream().Value()) {
-          << sequence_->DebugString();
+          // These checks only make sense if any values are not PostStream, but
-      RET_CHECK_GT(last_timestamp_seen, Timestamp::PreStream().Value())
+          // only need to be made once.
-          << "Something went wrong because the last timestamp is unset. "
+          RET_CHECK(!last_timestamp_key_.empty())
-             "Example: "
+              << "Something went wrong because the timestamp key is unset. "
-          << sequence_->DebugString();
+              << "Example: " << sequence_->DebugString();
-      RET_CHECK_LT(first_timestamp_seen_,
+          RET_CHECK_GT(last_timestamp_seen, Timestamp::PreStream().Value())
-                   Timestamp::OneOverPostStream().Value())
+              << "Something went wrong because the last timestamp is unset. "
-          << "Something went wrong because the first timestamp is unset. "
+              << "Example: " << sequence_->DebugString();
-             "Example: "
+          RET_CHECK_LT(first_timestamp_seen_,
-          << sequence_->DebugString();
+                       Timestamp::OneOverPostStream().Value())
+              << "Something went wrong because the first timestamp is unset. "
+              << "Example: " << sequence_->DebugString();
+          break;
+        }
+      }
     }
     current_timestamp_index_ = 0;
+    process_poststream_ = false;
 
     // Determine the data path and output it.
     const auto& options = cc->Options<UnpackMediaSequenceCalculatorOptions>();
     const auto& sequence = cc->InputSidePackets()
                                .Tag(kSequenceExampleTag)
                                .Get<tensorflow::SequenceExample>();
-    if (cc->Outputs().HasTag(kKeypointsTag)) {
-      keypoint_names_ = absl::StrSplit(options.keypoint_names(), ',');
-      default_keypoint_location_ = options.default_keypoint_location();
-    }
     if (cc->OutputSidePackets().HasTag(kDataPath)) {
       std::string root_directory = "";
       if (cc->InputSidePackets().HasTag(kDatasetRootDirTag)) {
@@ -349,19 +352,30 @@ class UnpackMediaSequenceCalculator : public CalculatorBase {
     // all packets on all streams that have a timestamp between the current
     // reference timestep and the previous reference timestep. This ensures that
     // we emit all timestamps in order, but also only emit a limited number in
-    // any particular call to Process().
+    // any particular call to Process(). At the every end, we output the
-    int64 start_timestamp =
+    // poststream packets. If we only have poststream packets,
-        timestamps_[last_timestamp_key_][current_timestamp_index_];
+    // last_timestamp_key_ will be empty.
-    if (current_timestamp_index_ == 0) {
+    int64 start_timestamp = 0;
-      start_timestamp = first_timestamp_seen_;
+    int64 end_timestamp = 0;
+    if (last_timestamp_key_.empty() || process_poststream_) {
+      process_poststream_ = true;
+      start_timestamp = Timestamp::PostStream().Value();
+      end_timestamp = Timestamp::OneOverPostStream().Value();
+    } else {
+      start_timestamp =
+          timestamps_[last_timestamp_key_][current_timestamp_index_];
+      if (current_timestamp_index_ == 0) {
+        start_timestamp = first_timestamp_seen_;
+      }
+
+      end_timestamp = start_timestamp + 1;  // Base case at end of sequence.
+      if (current_timestamp_index_ <
+          timestamps_[last_timestamp_key_].size() - 1) {
+        end_timestamp =
+            timestamps_[last_timestamp_key_][current_timestamp_index_ + 1];
+      }
     }
 
-    int64 end_timestamp = start_timestamp + 1;  // Base case at end of sequence.
-    if (current_timestamp_index_ <
-        timestamps_[last_timestamp_key_].size() - 1) {
-      end_timestamp =
-          timestamps_[last_timestamp_key_][current_timestamp_index_ + 1];
-    }
     for (const auto& map_kv : timestamps_) {
       for (int i = 0; i < map_kv.second.size(); ++i) {
         if (map_kv.second[i] >= start_timestamp &&
@@ -438,7 +452,14 @@ class UnpackMediaSequenceCalculator : public CalculatorBase {
     if (current_timestamp_index_ < timestamps_[last_timestamp_key_].size()) {
       return absl::OkStatus();
     } else {
-      return tool::StatusStop();
+      if (process_poststream_) {
+        // Once we've processed the PostStream timestamp we can stop.
+        return tool::StatusStop();
+      } else {
+        // Otherwise, we still need to do one more pass to process it.
+        process_poststream_ = true;
+        return absl::OkStatus();
+      }
     }
   }
 
@@ -462,6 +483,7 @@ class UnpackMediaSequenceCalculator : public CalculatorBase {
   std::vector<std::string> keypoint_names_;
   // Default keypoint location when missing.
   float default_keypoint_location_;
+  bool process_poststream_;
 };
 REGISTER_CALCULATOR(UnpackMediaSequenceCalculator);
 }  // namespace mediapipe

mediapipe/calculators/tensorflow/unpack_media_sequence_calculator_test.cc

@@ -412,6 +412,72 @@ TEST_F(UnpackMediaSequenceCalculatorTest, UnpacksTwoPostStreamFloatLists) {
               ::testing::Eq(Timestamp::PostStream()));
 }
 
+TEST_F(UnpackMediaSequenceCalculatorTest, UnpacksImageWithPostStreamFloatList) {
+  SetUpCalculator({"IMAGE:images"}, {});
+  auto input_sequence = absl::make_unique<tf::SequenceExample>();
+  std::string test_video_id = "test_video_id";
+  mpms::SetClipMediaId(test_video_id, input_sequence.get());
+
+  std::string test_image_string = "test_image_string";
+
+  int num_images = 1;
+  for (int i = 0; i < num_images; ++i) {
+    mpms::AddImageTimestamp(i, input_sequence.get());
+    mpms::AddImageEncoded(test_image_string, input_sequence.get());
+  }
+
+  mpms::AddFeatureFloats("FDENSE_MAX", {3.0f, 4.0f}, input_sequence.get());
+  mpms::AddFeatureTimestamp("FDENSE_MAX", Timestamp::PostStream().Value(),
+                            input_sequence.get());
+
+  runner_->MutableSidePackets()->Tag("SEQUENCE_EXAMPLE") =
+      Adopt(input_sequence.release());
+
+  MP_ASSERT_OK(runner_->Run());
+
+  const std::vector<Packet>& output_packets =
+      runner_->Outputs().Tag("IMAGE").packets;
+  ASSERT_EQ(num_images, output_packets.size());
+
+  for (int i = 0; i < num_images; ++i) {
+    const std::string& output_image = output_packets[i].Get<std::string>();
+    ASSERT_EQ(output_image, test_image_string);
+  }
+}
+
+TEST_F(UnpackMediaSequenceCalculatorTest, UnpacksPostStreamFloatListWithImage) {
+  SetUpCalculator({"FLOAT_FEATURE_FDENSE_MAX:max"}, {});
+  auto input_sequence = absl::make_unique<tf::SequenceExample>();
+  std::string test_video_id = "test_video_id";
+  mpms::SetClipMediaId(test_video_id, input_sequence.get());
+
+  std::string test_image_string = "test_image_string";
+
+  int num_images = 1;
+  for (int i = 0; i < num_images; ++i) {
+    mpms::AddImageTimestamp(i, input_sequence.get());
+    mpms::AddImageEncoded(test_image_string, input_sequence.get());
+  }
+
+  mpms::AddFeatureFloats("FDENSE_MAX", {3.0f, 4.0f}, input_sequence.get());
+  mpms::AddFeatureTimestamp("FDENSE_MAX", Timestamp::PostStream().Value(),
+                            input_sequence.get());
+
+  runner_->MutableSidePackets()->Tag("SEQUENCE_EXAMPLE") =
+      Adopt(input_sequence.release());
+
+  MP_ASSERT_OK(runner_->Run());
+
+  const std::vector<Packet>& fdense_max_packets =
+      runner_->Outputs().Tag("FLOAT_FEATURE_FDENSE_MAX").packets;
+  ASSERT_EQ(fdense_max_packets.size(), 1);
+  const auto& fdense_max_vector =
+      fdense_max_packets[0].Get<std::vector<float>>();
+  ASSERT_THAT(fdense_max_vector, ::testing::ElementsAreArray({3.0f, 4.0f}));
+  ASSERT_THAT(fdense_max_packets[0].Timestamp(),
+              ::testing::Eq(Timestamp::PostStream()));
+}
+
 TEST_F(UnpackMediaSequenceCalculatorTest, GetDatasetFromPacket) {
   SetUpCalculator({}, {"DATA_PATH:data_path"}, {"DATASET_ROOT:root"});
 

mediapipe/calculators/tflite/tflite_inference_calculator.cc

@@ -904,7 +904,8 @@ absl::Status TfLiteInferenceCalculator::LoadDelegate(CalculatorContext* cc) {
 #if MEDIAPIPE_TFLITE_GL_INFERENCE
   // Configure and create the delegate.
   TfLiteGpuDelegateOptions options = TfLiteGpuDelegateOptionsDefault();
-  options.compile_options.precision_loss_allowed = 1;
+  options.compile_options.precision_loss_allowed =
+      allow_precision_loss_ ? 1 : 0;
   options.compile_options.preferred_gl_object_type =
       TFLITE_GL_OBJECT_TYPE_FASTEST;
   options.compile_options.dynamic_batch_enabled = 0;
@@ -968,7 +969,7 @@ absl::Status TfLiteInferenceCalculator::LoadDelegate(CalculatorContext* cc) {
   const int kHalfSize = 2;  // sizeof(half)
   // Configure and create the delegate.
   TFLGpuDelegateOptions options;
-  options.allow_precision_loss = true;
+  options.allow_precision_loss = allow_precision_loss_;
   options.wait_type = TFLGpuDelegateWaitType::TFLGpuDelegateWaitTypeActive;
   if (!delegate_)
     delegate_ = TfLiteDelegatePtr(TFLGpuDelegateCreate(&options),
@@ -1080,9 +1081,10 @@ absl::Status TfLiteInferenceCalculator::LoadDelegate(CalculatorContext* cc) {
     }
 
     // Create converter for GPU output.
-    converter_from_BPHWC4_ = [[TFLBufferConvert alloc] initWithDevice:device
+    converter_from_BPHWC4_ =
-                                                            isFloat16:true
+        [[TFLBufferConvert alloc] initWithDevice:device
-                                                      convertToPBHWC4:false];
+                                       isFloat16:allow_precision_loss_
+                                 convertToPBHWC4:false];
     if (converter_from_BPHWC4_ == nil) {
       return absl::InternalError(
           "Error initializating output buffer converter");

mediapipe/calculators/tflite/tflite_tensors_to_segmentation_calculator.cc

@@ -439,7 +439,7 @@ absl::Status TfLiteTensorsToSegmentationCalculator::ProcessGpu(
 
   // Run shader, upsample result.
   {
-    gpu_helper_.BindFramebuffer(output_texture);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(output_texture);
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(GL_TEXTURE_2D, small_mask_texture.id());
     GlRender();

mediapipe/calculators/util/BUILD

@@ -821,6 +821,25 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_test(
+    name = "landmark_projection_calculator_test",
+    srcs = ["landmark_projection_calculator_test.cc"],
+    deps = [
+        ":landmark_projection_calculator",
+        "//mediapipe/calculators/tensor:image_to_tensor_utils",
+        "//mediapipe/framework:calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework:calculator_runner",
+        "//mediapipe/framework/deps:message_matchers",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:rect_cc_proto",
+        "//mediapipe/framework/port:gtest_main",
+        "//mediapipe/framework/port:parse_text_proto",
+        "@com_google_absl//absl/memory",
+        "@com_google_googletest//:gtest_main",
+    ],
+)
+
 mediapipe_proto_library(
     name = "landmarks_smoothing_calculator_proto",
     srcs = ["landmarks_smoothing_calculator.proto"],
@@ -1252,3 +1271,45 @@ cc_test(
         "//mediapipe/framework/port:parse_text_proto",
     ],
 )
+
+mediapipe_proto_library(
+    name = "refine_landmarks_from_heatmap_calculator_proto",
+    srcs = ["refine_landmarks_from_heatmap_calculator.proto"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/framework:calculator_options_proto",
+        "//mediapipe/framework:calculator_proto",
+    ],
+)
+
+cc_library(
+    name = "refine_landmarks_from_heatmap_calculator",
+    srcs = ["refine_landmarks_from_heatmap_calculator.cc"],
+    hdrs = ["refine_landmarks_from_heatmap_calculator.h"],
+    copts = select({
+        "//mediapipe:apple": [
+            "-x objective-c++",
+            "-fobjc-arc",  # enable reference-counting
+        ],
+        "//conditions:default": [],
+    }),
+    visibility = ["//visibility:public"],
+    deps = [
+        ":refine_landmarks_from_heatmap_calculator_cc_proto",
+        "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/api2:node",
+        "//mediapipe/framework/formats:landmark_cc_proto",
+        "//mediapipe/framework/formats:tensor",
+        "//mediapipe/framework/port:statusor",
+    ],
+    alwayslink = 1,
+)
+
+cc_test(
+    name = "refine_landmarks_from_heatmap_calculator_test",
+    srcs = ["refine_landmarks_from_heatmap_calculator_test.cc"],
+    deps = [
+        ":refine_landmarks_from_heatmap_calculator",
+        "//mediapipe/framework/port:gtest_main",
+    ],
+)

mediapipe/calculators/util/annotation_overlay_calculator.cc

@@ -402,7 +402,7 @@ absl::Status AnnotationOverlayCalculator::RenderToGpu(CalculatorContext* cc,
 
   // Blend overlay image in GPU shader.
   {
-    gpu_helper_.BindFramebuffer(output_texture);  // GL_TEXTURE0
+    gpu_helper_.BindFramebuffer(output_texture);
 
     glActiveTexture(GL_TEXTURE1);
     glBindTexture(GL_TEXTURE_2D, input_texture.name());

mediapipe/calculators/util/detection_label_id_to_text_calculator.cc

@@ -54,6 +54,7 @@ class DetectionLabelIdToTextCalculator : public CalculatorBase {
 
  private:
   absl::node_hash_map<int, std::string> label_map_;
+  ::mediapipe::DetectionLabelIdToTextCalculatorOptions options_;
 };
 REGISTER_CALCULATOR(DetectionLabelIdToTextCalculator);
 
@@ -68,13 +69,13 @@ absl::Status DetectionLabelIdToTextCalculator::GetContract(
 absl::Status DetectionLabelIdToTextCalculator::Open(CalculatorContext* cc) {
   cc->SetOffset(TimestampDiff(0));
 
-  const auto& options =
+  options_ =
       cc->Options<::mediapipe::DetectionLabelIdToTextCalculatorOptions>();
 
-  if (options.has_label_map_path()) {
+  if (options_.has_label_map_path()) {
     std::string string_path;
     ASSIGN_OR_RETURN(string_path,
-                     PathToResourceAsFile(options.label_map_path()));
+                     PathToResourceAsFile(options_.label_map_path()));
     std::string label_map_string;
     MP_RETURN_IF_ERROR(file::GetContents(string_path, &label_map_string));
 
@@ -85,8 +86,8 @@ absl::Status DetectionLabelIdToTextCalculator::Open(CalculatorContext* cc) {
       label_map_[i++] = line;
     }
   } else {
-    for (int i = 0; i < options.label_size(); ++i) {
+    for (int i = 0; i < options_.label_size(); ++i) {
-      label_map_[i] = options.label(i);
+      label_map_[i] = options_.label(i);
     }
   }
   return absl::OkStatus();
@@ -106,7 +107,7 @@ absl::Status DetectionLabelIdToTextCalculator::Process(CalculatorContext* cc) {
       }
     }
     // Remove label_id field if text labels exist.
-    if (has_text_label) {
+    if (has_text_label && !options_.keep_label_id()) {
       output_detection.clear_label_id();
     }
   }

mediapipe/calculators/util/detection_label_id_to_text_calculator.proto

@@ -31,4 +31,9 @@ message DetectionLabelIdToTextCalculatorOptions {
   // label: "label for id 1"
   // ...
   repeated string label = 2;
+
+  // By default, the `label_id` field from the input is stripped if a text label
+  // could be found. By setting this field to true, it is always copied to the
+  // output detections.
+  optional bool keep_label_id = 3;
 }

mediapipe/calculators/util/labels_to_render_data_calculator.cc

@@ -120,7 +120,11 @@ absl::Status LabelsToRenderDataCalculator::Process(CalculatorContext* cc) {
     labels.resize(classifications.classification_size());
     scores.resize(classifications.classification_size());
     for (int i = 0; i < classifications.classification_size(); ++i) {
-      labels[i] = classifications.classification(i).label();
+      if (options_.use_display_name()) {
+        labels[i] = classifications.classification(i).display_name();
+      } else {
+        labels[i] = classifications.classification(i).label();
+      }
       scores[i] = classifications.classification(i).score();
     }
   } else {

mediapipe/calculators/util/labels_to_render_data_calculator.proto

@@ -59,4 +59,7 @@ message LabelsToRenderDataCalculatorOptions {
     BOTTOM_LEFT = 1;
   }
   optional Location location = 6 [default = TOP_LEFT];
+
+  // Uses Classification.display_name field instead of Classification.label.
+  optional bool use_display_name = 9 [default = false];
 }

mediapipe/calculators/util/landmark_projection_calculator.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include <cmath>
+#include <functional>
 #include <vector>
 
 #include "mediapipe/calculators/util/landmark_projection_calculator.pb.h"
@@ -27,20 +28,32 @@ namespace {
 
 constexpr char kLandmarksTag[] = "NORM_LANDMARKS";
 constexpr char kRectTag[] = "NORM_RECT";
+constexpr char kProjectionMatrix[] = "PROJECTION_MATRIX";
 
 }  // namespace
 
-// Projects normalized landmarks in a rectangle to its original coordinates. The
+// Projects normalized landmarks to its original coordinates.
-// rectangle must also be in normalized coordinates.
 // Input:
-//   NORM_LANDMARKS: A NormalizedLandmarkList representing landmarks
+//   NORM_LANDMARKS - NormalizedLandmarkList
-//                   in a normalized rectangle.
+//     Represents landmarks in a normalized rectangle if NORM_RECT is specified
-//   NORM_RECT: An NormalizedRect representing a normalized rectangle in image
+//     or landmarks that should be projected using PROJECTION_MATRIX if
-//              coordinates.
+//     specified. (Prefer using PROJECTION_MATRIX as it eliminates need of
+//     letterbox removal step.)
+//   NORM_RECT - NormalizedRect
+//     Represents a normalized rectangle in image coordinates and results in
+//     landmarks with their locations adjusted to the image.
+//   PROJECTION_MATRIX - std::array<float, 16>
+//     A 4x4 row-major-order matrix that maps landmarks' locations from one
+//     coordinate system to another. In this case from the coordinate system of
+//     the normalized region of interest to the coordinate system of the image.
+//
+//   Note: either NORM_RECT or PROJECTION_MATRIX has to be specified.
+//   Note: landmark's Z is projected in a custom way - it's scaled by width of
+//     the normalized region of interest used during landmarks detection.
 //
 // Output:
-//   NORM_LANDMARKS: A NormalizedLandmarkList representing landmarks
+//   NORM_LANDMARKS - NormalizedLandmarkList
-//                   with their locations adjusted to the image.
+//     Landmarks with their locations adjusted according to the inputs.
 //
 // Usage example:
 // node {
@@ -58,12 +71,27 @@ constexpr char kRectTag[] = "NORM_RECT";
 //   output_stream: "NORM_LANDMARKS:0:projected_landmarks_0"
 //   output_stream: "NORM_LANDMARKS:1:projected_landmarks_1"
 // }
+//
+// node {
+//   calculator: "LandmarkProjectionCalculator"
+//   input_stream: "NORM_LANDMARKS:landmarks"
+//   input_stream: "PROECTION_MATRIX:matrix"
+//   output_stream: "NORM_LANDMARKS:projected_landmarks"
+// }
+//
+// node {
+//   calculator: "LandmarkProjectionCalculator"
+//   input_stream: "NORM_LANDMARKS:0:landmarks_0"
+//   input_stream: "NORM_LANDMARKS:1:landmarks_1"
+//   input_stream: "PROECTION_MATRIX:matrix"
+//   output_stream: "NORM_LANDMARKS:0:projected_landmarks_0"
+//   output_stream: "NORM_LANDMARKS:1:projected_landmarks_1"
+// }
 class LandmarkProjectionCalculator : public CalculatorBase {
  public:
   static absl::Status GetContract(CalculatorContract* cc) {
-    RET_CHECK(cc->Inputs().HasTag(kLandmarksTag) &&
+    RET_CHECK(cc->Inputs().HasTag(kLandmarksTag))
-              cc->Inputs().HasTag(kRectTag))
+        << "Missing NORM_LANDMARKS input.";
-        << "Missing one or more input streams.";
 
     RET_CHECK_EQ(cc->Inputs().NumEntries(kLandmarksTag),
                  cc->Outputs().NumEntries(kLandmarksTag))
@@ -73,7 +101,14 @@ class LandmarkProjectionCalculator : public CalculatorBase {
          id != cc->Inputs().EndId(kLandmarksTag); ++id) {
       cc->Inputs().Get(id).Set<NormalizedLandmarkList>();
     }
-    cc->Inputs().Tag(kRectTag).Set<NormalizedRect>();
+    RET_CHECK(cc->Inputs().HasTag(kRectTag) ^
+              cc->Inputs().HasTag(kProjectionMatrix))
+        << "Either NORM_RECT or PROJECTION_MATRIX must be specified.";
+    if (cc->Inputs().HasTag(kRectTag)) {
+      cc->Inputs().Tag(kRectTag).Set<NormalizedRect>();
+    } else {
+      cc->Inputs().Tag(kProjectionMatrix).Set<std::array<float, 16>>();
+    }
 
     for (CollectionItemId id = cc->Outputs().BeginId(kLandmarksTag);
          id != cc->Outputs().EndId(kLandmarksTag); ++id) {
@@ -89,31 +124,50 @@ class LandmarkProjectionCalculator : public CalculatorBase {
     return absl::OkStatus();
   }
 
+  static void ProjectXY(const NormalizedLandmark& lm,
+                        const std::array<float, 16>& matrix,
+                        NormalizedLandmark* out) {
+    out->set_x(lm.x() * matrix[0] + lm.y() * matrix[1] + lm.z() * matrix[2] +
+               matrix[3]);
+    out->set_y(lm.x() * matrix[4] + lm.y() * matrix[5] + lm.z() * matrix[6] +
+               matrix[7]);
+  }
+
+  /**
+   * Landmark's Z scale is equal to a relative (to image) width of region of
+   * interest used during detection. To calculate based on matrix:
+   * 1. Project (0,0) --- (1,0) segment using matrix.
+   * 2. Calculate length of the projected segment.
+   */
+  static float CalculateZScale(const std::array<float, 16>& matrix) {
+    NormalizedLandmark a;
+    a.set_x(0.0f);
+    a.set_y(0.0f);
+    NormalizedLandmark b;
+    b.set_x(1.0f);
+    b.set_y(0.0f);
+    NormalizedLandmark a_projected;
+    ProjectXY(a, matrix, &a_projected);
+    NormalizedLandmark b_projected;
+    ProjectXY(b, matrix, &b_projected);
+    return std::sqrt(std::pow(b_projected.x() - a_projected.x(), 2) +
+                     std::pow(b_projected.y() - a_projected.y(), 2));
+  }
+
   absl::Status Process(CalculatorContext* cc) override {
-    if (cc->Inputs().Tag(kRectTag).IsEmpty()) {
+    std::function<void(const NormalizedLandmark&, NormalizedLandmark*)>
-      return absl::OkStatus();
+        project_fn;
-    }
+    if (cc->Inputs().HasTag(kRectTag)) {
-    const auto& input_rect = cc->Inputs().Tag(kRectTag).Get<NormalizedRect>();
+      if (cc->Inputs().Tag(kRectTag).IsEmpty()) {
-
+        return absl::OkStatus();
-    const auto& options =
-        cc->Options<::mediapipe::LandmarkProjectionCalculatorOptions>();
-
-    CollectionItemId input_id = cc->Inputs().BeginId(kLandmarksTag);
-    CollectionItemId output_id = cc->Outputs().BeginId(kLandmarksTag);
-    // Number of inputs and outpus is the same according to the contract.
-    for (; input_id != cc->Inputs().EndId(kLandmarksTag);
-         ++input_id, ++output_id) {
-      const auto& input_packet = cc->Inputs().Get(input_id);
-      if (input_packet.IsEmpty()) {
-        continue;
       }
-
+      const auto& input_rect = cc->Inputs().Tag(kRectTag).Get<NormalizedRect>();
-      const auto& input_landmarks = input_packet.Get<NormalizedLandmarkList>();
+      const auto& options =
-      NormalizedLandmarkList output_landmarks;
+          cc->Options<mediapipe::LandmarkProjectionCalculatorOptions>();
-      for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
+      project_fn = [&input_rect, &options](const NormalizedLandmark& landmark,
-        const NormalizedLandmark& landmark = input_landmarks.landmark(i);
+                                           NormalizedLandmark* new_landmark) {
-        NormalizedLandmark* new_landmark = output_landmarks.add_landmark();
+        // TODO: fix projection or deprecate (current projection
-
+        // calculations are incorrect for general case).
         const float x = landmark.x() - 0.5f;
         const float y = landmark.y() - 0.5f;
         const float angle =
@@ -130,10 +184,44 @@ class LandmarkProjectionCalculator : public CalculatorBase {
         new_landmark->set_x(new_x);
         new_landmark->set_y(new_y);
         new_landmark->set_z(new_z);
+      };
+    } else if (cc->Inputs().HasTag(kProjectionMatrix)) {
+      if (cc->Inputs().Tag(kProjectionMatrix).IsEmpty()) {
+        return absl::OkStatus();
+      }
+      const auto& project_mat =
+          cc->Inputs().Tag(kProjectionMatrix).Get<std::array<float, 16>>();
+      const float z_scale = CalculateZScale(project_mat);
+      project_fn = [&project_mat, z_scale](const NormalizedLandmark& lm,
+                                           NormalizedLandmark* new_landmark) {
+        *new_landmark = lm;
+        ProjectXY(lm, project_mat, new_landmark);
+        new_landmark->set_z(z_scale * lm.z());
+      };
+    } else {
+      return absl::InternalError("Either rect or matrix must be specified.");
+    }
+
+    CollectionItemId input_id = cc->Inputs().BeginId(kLandmarksTag);
+    CollectionItemId output_id = cc->Outputs().BeginId(kLandmarksTag);
+    // Number of inputs and outpus is the same according to the contract.
+    for (; input_id != cc->Inputs().EndId(kLandmarksTag);
+         ++input_id, ++output_id) {
+      const auto& input_packet = cc->Inputs().Get(input_id);
+      if (input_packet.IsEmpty()) {
+        continue;
+      }
+
+      const auto& input_landmarks = input_packet.Get<NormalizedLandmarkList>();
+      NormalizedLandmarkList output_landmarks;
+      for (int i = 0; i < input_landmarks.landmark_size(); ++i) {
+        const NormalizedLandmark& landmark = input_landmarks.landmark(i);
+        NormalizedLandmark* new_landmark = output_landmarks.add_landmark();
+        project_fn(landmark, new_landmark);
       }
 
       cc->Outputs().Get(output_id).AddPacket(
-          MakePacket<NormalizedLandmarkList>(output_landmarks)
+          MakePacket<NormalizedLandmarkList>(std::move(output_landmarks))
               .At(cc->InputTimestamp()));
     }
     return absl::OkStatus();

mediapipe/calculators/util/landmark_projection_calculator_test.cc

@@ -0,0 +1,240 @@
+#include <array>
+#include <vector>
+
+#include "absl/memory/memory.h"
+#include "mediapipe/calculators/tensor/image_to_tensor_utils.h"
+#include "mediapipe/framework/calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+#include "mediapipe/framework/calculator_runner.h"
+#include "mediapipe/framework/deps/message_matchers.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/rect.pb.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/parse_text_proto.h"
+#include "mediapipe/framework/port/status_matchers.h"
+
+namespace mediapipe {
+namespace {
+
+absl::StatusOr<mediapipe::NormalizedLandmarkList> RunCalculator(
+    mediapipe::NormalizedLandmarkList input, mediapipe::NormalizedRect rect) {
+  mediapipe::CalculatorRunner runner(
+      ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig::Node>(R"pb(
+        calculator: "LandmarkProjectionCalculator"
+        input_stream: "NORM_LANDMARKS:landmarks"
+        input_stream: "NORM_RECT:rect"
+        output_stream: "NORM_LANDMARKS:projected_landmarks"
+      )pb"));
+  runner.MutableInputs()
+      ->Tag("NORM_LANDMARKS")
+      .packets.push_back(
+          MakePacket<mediapipe::NormalizedLandmarkList>(std::move(input))
+              .At(Timestamp(1)));
+  runner.MutableInputs()
+      ->Tag("NORM_RECT")
+      .packets.push_back(MakePacket<mediapipe::NormalizedRect>(std::move(rect))
+                             .At(Timestamp(1)));
+
+  MP_RETURN_IF_ERROR(runner.Run());
+  const auto& output_packets = runner.Outputs().Tag("NORM_LANDMARKS").packets;
+  RET_CHECK_EQ(output_packets.size(), 1);
+  return output_packets[0].Get<mediapipe::NormalizedLandmarkList>();
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithDefaultRect) {
+  mediapipe::NormalizedLandmarkList landmarks =
+      ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+      )pb");
+  mediapipe::NormalizedRect rect =
+      ParseTextProtoOrDie<mediapipe::NormalizedRect>(
+          R"pb(
+            x_center: 0.5,
+            y_center: 0.5,
+            width: 1.0,
+            height: 1.0,
+            rotation: 0.0
+          )pb");
+
+  auto status_or_result = RunCalculator(std::move(landmarks), std::move(rect));
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(
+      status_or_result.value(),
+      EqualsProto(ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+      )pb")));
+}
+
+mediapipe::NormalizedRect GetCroppedRect() {
+  return ParseTextProtoOrDie<mediapipe::NormalizedRect>(
+      R"pb(
+        x_center: 0.5, y_center: 0.5, width: 0.5, height: 2, rotation: 0.0
+      )pb");
+}
+
+mediapipe::NormalizedLandmarkList GetCroppedRectTestInput() {
+  return ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+    landmark { x: 1.0, y: 1.0, z: -0.5 }
+  )pb");
+}
+
+mediapipe::NormalizedLandmarkList GetCroppedRectTestExpectedResult() {
+  return ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+    landmark { x: 0.75, y: 1.5, z: -0.25 }
+  )pb");
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithCroppedRect) {
+  auto status_or_result =
+      RunCalculator(GetCroppedRectTestInput(), GetCroppedRect());
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(status_or_result.value(),
+              EqualsProto(GetCroppedRectTestExpectedResult()));
+}
+
+absl::StatusOr<mediapipe::NormalizedLandmarkList> RunCalculator(
+    mediapipe::NormalizedLandmarkList input, std::array<float, 16> matrix) {
+  mediapipe::CalculatorRunner runner(
+      ParseTextProtoOrDie<mediapipe::CalculatorGraphConfig::Node>(R"pb(
+        calculator: "LandmarkProjectionCalculator"
+        input_stream: "NORM_LANDMARKS:landmarks"
+        input_stream: "PROJECTION_MATRIX:matrix"
+        output_stream: "NORM_LANDMARKS:projected_landmarks"
+      )pb"));
+  runner.MutableInputs()
+      ->Tag("NORM_LANDMARKS")
+      .packets.push_back(
+          MakePacket<mediapipe::NormalizedLandmarkList>(std::move(input))
+              .At(Timestamp(1)));
+  runner.MutableInputs()
+      ->Tag("PROJECTION_MATRIX")
+      .packets.push_back(MakePacket<std::array<float, 16>>(std::move(matrix))
+                             .At(Timestamp(1)));
+
+  MP_RETURN_IF_ERROR(runner.Run());
+  const auto& output_packets = runner.Outputs().Tag("NORM_LANDMARKS").packets;
+  RET_CHECK_EQ(output_packets.size(), 1);
+  return output_packets[0].Get<mediapipe::NormalizedLandmarkList>();
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithIdentityMatrix) {
+  mediapipe::NormalizedLandmarkList landmarks =
+      ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+      )pb");
+  // clang-format off
+  std::array<float, 16> matrix = {
+    1.0f, 0.0f, 0.0f, 0.0f,
+    0.0f, 1.0f, 0.0f, 0.0f,
+    0.0f, 0.0f, 1.0f, 0.0f,
+    0.0f, 0.0f, 0.0f, 1.0f,
+  };
+  // clang-format on
+
+  auto status_or_result =
+      RunCalculator(std::move(landmarks), std::move(matrix));
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(
+      status_or_result.value(),
+      EqualsProto(ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+      )pb")));
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithCroppedRectMatrix) {
+  constexpr int kRectWidth = 1280;
+  constexpr int kRectHeight = 720;
+  auto roi = GetRoi(kRectWidth, kRectHeight, GetCroppedRect());
+  std::array<float, 16> matrix;
+  GetRotatedSubRectToRectTransformMatrix(roi, kRectWidth, kRectHeight,
+                                         /*flip_horizontaly=*/false, &matrix);
+  auto status_or_result = RunCalculator(GetCroppedRectTestInput(), matrix);
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(status_or_result.value(),
+              EqualsProto(GetCroppedRectTestExpectedResult()));
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithScaleMatrix) {
+  mediapipe::NormalizedLandmarkList landmarks =
+      ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+        landmark { x: 5, y: 6, z: 7 }
+      )pb");
+  // clang-format off
+  std::array<float, 16> matrix = {
+    10.0f, 0.0f,   0.0f, 0.0f,
+    0.0f,  100.0f, 0.0f, 0.0f,
+    0.0f,  0.0f,   1.0f, 0.0f,
+    0.0f,  0.0f,   0.0f, 1.0f,
+  };
+  // clang-format on
+
+  auto status_or_result =
+      RunCalculator(std::move(landmarks), std::move(matrix));
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(
+      status_or_result.value(),
+      EqualsProto(ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 100, y: 2000, z: -5 }
+        landmark { x: 50, y: 600, z: 70 }
+      )pb")));
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithTranslateMatrix) {
+  mediapipe::NormalizedLandmarkList landmarks =
+      ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 10, y: 20, z: -0.5 }
+      )pb");
+  // clang-format off
+  std::array<float, 16> matrix = {
+    1.0f, 0.0f, 0.0f, 1.0f,
+    0.0f, 1.0f, 0.0f, 2.0f,
+    0.0f, 0.0f, 1.0f, 0.0f,
+    0.0f, 0.0f, 0.0f, 1.0f,
+  };
+  // clang-format on
+
+  auto status_or_result =
+      RunCalculator(std::move(landmarks), std::move(matrix));
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(
+      status_or_result.value(),
+      EqualsProto(ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 11, y: 22, z: -0.5 }
+      )pb")));
+}
+
+TEST(LandmarkProjectionCalculatorTest, ProjectingWithRotationMatrix) {
+  mediapipe::NormalizedLandmarkList landmarks =
+      ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 4, y: 0, z: -0.5 }
+      )pb");
+  // clang-format off
+  // 90 degrees rotation matrix
+  std::array<float, 16> matrix = {
+    0.0f, -1.0f, 0.0f, 0.0f,
+    1.0f,  0.0f, 0.0f, 0.0f,
+    0.0f,  0.0f, 1.0f, 0.0f,
+    0.0f,  0.0f, 0.0f, 1.0f,
+  };
+  // clang-format on
+
+  auto status_or_result =
+      RunCalculator(std::move(landmarks), std::move(matrix));
+  MP_ASSERT_OK(status_or_result);
+
+  EXPECT_THAT(
+      status_or_result.value(),
+      EqualsProto(ParseTextProtoOrDie<mediapipe::NormalizedLandmarkList>(R"pb(
+        landmark { x: 0, y: 4, z: -0.5 }
+      )pb")));
+}
+
+}  // namespace
+}  // namespace mediapipe

mediapipe/calculators/util/rect_to_render_scale_calculator.cc

@@ -1,3 +1,17 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include "mediapipe/calculators/util/rect_to_render_scale_calculator.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/rect.pb.h"

mediapipe/calculators/util/rect_to_render_scale_calculator.proto

@@ -1,3 +1,17 @@
+// Copyright 2020 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 syntax = "proto2";
 
 package mediapipe;

mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.cc

@@ -0,0 +1,166 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.h"
+
+#include "mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.pb.h"
+#include "mediapipe/framework/calculator_framework.h"
+
+namespace mediapipe {
+
+namespace {
+
+inline float Sigmoid(float value) { return 1.0f / (1.0f + std::exp(-value)); }
+
+absl::StatusOr<std::tuple<int, int, int>> GetHwcFromDims(
+    const std::vector<int>& dims) {
+  if (dims.size() == 3) {
+    return std::make_tuple(dims[0], dims[1], dims[2]);
+  } else if (dims.size() == 4) {
+    // BHWC format check B == 1
+    RET_CHECK_EQ(1, dims[0]) << "Expected batch to be 1 for BHWC heatmap";
+    return std::make_tuple(dims[1], dims[2], dims[3]);
+  } else {
+    RET_CHECK(false) << "Invalid shape size for heatmap tensor" << dims.size();
+  }
+}
+
+}  // namespace
+
+namespace api2 {
+
+// Refines landmarks using correspond heatmap area.
+//
+// Input:
+//   NORM_LANDMARKS - Required. Input normalized landmarks to update.
+//   TENSORS - Required. Vector of input tensors. 0th element should be heatmap.
+//             The rest is unused.
+// Output:
+//   NORM_LANDMARKS - Required. Updated normalized landmarks.
+class RefineLandmarksFromHeatmapCalculatorImpl
+    : public NodeImpl<RefineLandmarksFromHeatmapCalculator,
+                      RefineLandmarksFromHeatmapCalculatorImpl> {
+ public:
+  absl::Status Open(CalculatorContext* cc) override { return absl::OkStatus(); }
+
+  absl::Status Process(CalculatorContext* cc) override {
+    // Make sure we bypass landmarks if there is no detection.
+    if (kInLandmarks(cc).IsEmpty()) {
+      return absl::OkStatus();
+    }
+    // If for some reason heatmap is missing, just return original landmarks.
+    if (kInTensors(cc).IsEmpty()) {
+      kOutLandmarks(cc).Send(*kInLandmarks(cc));
+      return absl::OkStatus();
+    }
+
+    // Check basic prerequisites.
+    const auto& input_tensors = *kInTensors(cc);
+    RET_CHECK(!input_tensors.empty()) << "Empty input tensors list. First "
+                                         "element is expeced to be a heatmap";
+
+    const auto& hm_tensor = input_tensors[0];
+    const auto& in_lms = *kInLandmarks(cc);
+    auto hm_view = hm_tensor.GetCpuReadView();
+    auto hm_raw = hm_view.buffer<float>();
+    const auto& options =
+        cc->Options<mediapipe::RefineLandmarksFromHeatmapCalculatorOptions>();
+
+    ASSIGN_OR_RETURN(auto out_lms, RefineLandmarksFromHeatMap(
+                                       in_lms, hm_raw, hm_tensor.shape().dims,
+                                       options.kernel_size(),
+                                       options.min_confidence_to_refine()));
+
+    kOutLandmarks(cc).Send(std::move(out_lms));
+    return absl::OkStatus();
+  }
+};
+
+}  // namespace api2
+
+// Runs actual refinement
+// High level algorithm:
+//
+// Heatmap is accepted as tensor in HWC layout where i-th channel is a heatmap
+// for the i-th landmark.
+//
+// For each landmark we replace original value with a value calculated from the
+// area in heatmap close to original landmark position (in particular are
+// covered with kernel of size options.kernel_size). To calculate new coordinate
+// from heatmap we calculate an weighted average inside the kernel. We update
+// the landmark iff heatmap is confident in it's prediction i.e. max(heatmap) in
+// kernel is at least options.min_confidence_to_refine big.
+absl::StatusOr<mediapipe::NormalizedLandmarkList> RefineLandmarksFromHeatMap(
+    const mediapipe::NormalizedLandmarkList& in_lms,
+    const float* heatmap_raw_data, const std::vector<int>& heatmap_dims,
+    int kernel_size, float min_confidence_to_refine) {
+  ASSIGN_OR_RETURN(auto hm_dims, GetHwcFromDims(heatmap_dims));
+  auto [hm_height, hm_width, hm_channels] = hm_dims;
+
+  RET_CHECK_EQ(in_lms.landmark_size(), hm_channels)
+      << "Expected heatmap to have number of layers == to number of "
+         "landmarks";
+
+  int hm_row_size = hm_width * hm_channels;
+  int hm_pixel_size = hm_channels;
+
+  mediapipe::NormalizedLandmarkList out_lms = in_lms;
+  for (int lm_index = 0; lm_index < out_lms.landmark_size(); ++lm_index) {
+    int center_col = out_lms.landmark(lm_index).x() * hm_width;
+    int center_row = out_lms.landmark(lm_index).y() * hm_height;
+    // Point is outside of the image let's keep it intact.
+    if (center_col < 0 || center_col >= hm_width || center_row < 0 ||
+        center_col >= hm_height) {
+      continue;
+    }
+
+    int offset = (kernel_size - 1) / 2;
+    // Calculate area to iterate over. Note that we decrease the kernel on
+    // the edges of the heatmap. Equivalent to zero border.
+    int begin_col = std::max(0, center_col - offset);
+    int end_col = std::min(hm_width, center_col + offset + 1);
+    int begin_row = std::max(0, center_row - offset);
+    int end_row = std::min(hm_height, center_row + offset + 1);
+
+    float sum = 0;
+    float weighted_col = 0;
+    float weighted_row = 0;
+    float max_value = 0;
+
+    // Main loop. Go over kernel and calculate weighted sum of coordinates,
+    // sum of weights and max weights.
+    for (int row = begin_row; row < end_row; ++row) {
+      for (int col = begin_col; col < end_col; ++col) {
+        // We expect memory to be in HWC layout without padding.
+        int idx = hm_row_size * row + hm_pixel_size * col + lm_index;
+        // Right now we hardcode sigmoid activation as it will be wasteful to
+        // calculate sigmoid for each value of heatmap in the model itself.  If
+        // we ever have other activations it should be trivial to expand via
+        // options.
+        float confidence = Sigmoid(heatmap_raw_data[idx]);
+        sum += confidence;
+        max_value = std::max(max_value, confidence);
+        weighted_col += col * confidence;
+        weighted_row += row * confidence;
+      }
+    }
+    if (max_value >= min_confidence_to_refine && sum > 0) {
+      out_lms.mutable_landmark(lm_index)->set_x(weighted_col / hm_width / sum);
+      out_lms.mutable_landmark(lm_index)->set_y(weighted_row / hm_height / sum);
+    }
+  }
+  return out_lms;
+}
+
+}  // namespace mediapipe

mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.h

@@ -0,0 +1,50 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef MEDIAPIPE_CALCULATORS_UTIL_REFINE_LANDMARKS_FROM_HEATMAP_CALCULATOR_H_
+#define MEDIAPIPE_CALCULATORS_UTIL_REFINE_LANDMARKS_FROM_HEATMAP_CALCULATOR_H_
+
+#include <vector>
+
+#include "mediapipe/framework/api2/node.h"
+#include "mediapipe/framework/formats/landmark.pb.h"
+#include "mediapipe/framework/formats/tensor.h"
+#include "mediapipe/framework/port/statusor.h"
+
+namespace mediapipe {
+namespace api2 {
+
+class RefineLandmarksFromHeatmapCalculator : public NodeIntf {
+ public:
+  static constexpr Input<mediapipe::NormalizedLandmarkList> kInLandmarks{
+      "NORM_LANDMARKS"};
+  static constexpr Input<std::vector<Tensor>> kInTensors{"TENSORS"};
+  static constexpr Output<mediapipe::NormalizedLandmarkList> kOutLandmarks{
+      "NORM_LANDMARKS"};
+
+  MEDIAPIPE_NODE_INTERFACE(RefineLandmarksFromHeatmapCalculator, kInLandmarks,
+                           kInTensors, kOutLandmarks);
+};
+
+}  // namespace api2
+
+// Exposed for testing.
+absl::StatusOr<mediapipe::NormalizedLandmarkList> RefineLandmarksFromHeatMap(
+    const mediapipe::NormalizedLandmarkList& in_lms,
+    const float* heatmap_raw_data, const std::vector<int>& heatmap_dims,
+    int kernel_size, float min_confidence_to_refine);
+
+}  // namespace mediapipe
+
+#endif  // MEDIAPIPE_CALCULATORS_UTIL_REFINE_LANDMARKS_FROM_HEATMAP_CALCULATOR_H_

mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.proto

@@ -0,0 +1,27 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mediapipe;
+
+import "mediapipe/framework/calculator.proto";
+
+message RefineLandmarksFromHeatmapCalculatorOptions {
+  extend mediapipe.CalculatorOptions {
+    optional RefineLandmarksFromHeatmapCalculatorOptions ext = 362281653;
+  }
+  optional int32 kernel_size = 1 [default = 9];
+  optional float min_confidence_to_refine = 2 [default = 0.5];
+}

mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator_test.cc

@@ -0,0 +1,152 @@
+// Copyright 2021 The MediaPipe Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "mediapipe/calculators/util/refine_landmarks_from_heatmap_calculator.h"
+
+#include "mediapipe/framework/port/gmock.h"
+#include "mediapipe/framework/port/gtest.h"
+#include "mediapipe/framework/port/status_matchers.h"
+
+namespace mediapipe {
+namespace {
+
+mediapipe::NormalizedLandmarkList vec_to_lms(
+    const std::vector<std::pair<float, float>>& inp) {
+  mediapipe::NormalizedLandmarkList ret;
+  for (const auto& it : inp) {
+    auto new_lm = ret.add_landmark();
+    new_lm->set_x(it.first);
+    new_lm->set_y(it.second);
+  }
+  return ret;
+}
+
+std::vector<std::pair<float, float>> lms_to_vec(
+    const mediapipe::NormalizedLandmarkList& lst) {
+  std::vector<std::pair<float, float>> ret;
+  for (const auto& lm : lst.landmark()) {
+    ret.push_back({lm.x(), lm.y()});
+  }
+  return ret;
+}
+
+std::vector<float> CHW_to_HWC(std::vector<float> inp, int height, int width,
+                              int depth) {
+  std::vector<float> ret(inp.size());
+  const float* inp_ptr = inp.data();
+  for (int c = 0; c < depth; ++c) {
+    for (int row = 0; row < height; ++row) {
+      for (int col = 0; col < width; ++col) {
+        int dest_idx = width * depth * row + depth * col + c;
+        ret[dest_idx] = *inp_ptr;
+        ++inp_ptr;
+      }
+    }
+  }
+  return ret;
+}
+
+using testing::ElementsAre;
+using testing::FloatEq;
+using testing::Pair;
+
+TEST(RefineLandmarksFromHeatmapTest, Smoke) {
+  float z = -10000000000000000;
+  // clang-format off
+  std::vector<float> hm = {
+    z, z, z,
+    1, z, z,
+    z, z, z};
+  // clang-format on
+
+  auto ret_or_error = RefineLandmarksFromHeatMap(vec_to_lms({{0.5, 0.5}}),
+                                                 hm.data(), {3, 3, 1}, 3, 0.1);
+  MP_EXPECT_OK(ret_or_error);
+  EXPECT_THAT(lms_to_vec(*ret_or_error),
+              ElementsAre(Pair(FloatEq(0), FloatEq(1 / 3.))));
+}
+
+TEST(RefineLandmarksFromHeatmapTest, MultiLayer) {
+  float z = -10000000000000000;
+  // clang-format off
+  std::vector<float> hm = CHW_to_HWC({
+    z, z, z,
+    1, z, z,
+    z, z, z,
+    z, z, z,
+    1, z, z,
+    z, z, z,
+    z, z, z,
+    1, z, z,
+    z, z, z}, 3, 3, 3);
+  // clang-format on
+
+  auto ret_or_error = RefineLandmarksFromHeatMap(
+      vec_to_lms({{0.5, 0.5}, {0.5, 0.5}, {0.5, 0.5}}), hm.data(), {3, 3, 3}, 3,
+      0.1);
+  MP_EXPECT_OK(ret_or_error);
+  EXPECT_THAT(lms_to_vec(*ret_or_error),
+              ElementsAre(Pair(FloatEq(0), FloatEq(1 / 3.)),
+                          Pair(FloatEq(0), FloatEq(1 / 3.)),
+                          Pair(FloatEq(0), FloatEq(1 / 3.))));
+}
+
+TEST(RefineLandmarksFromHeatmapTest, KeepIfNotSure) {
+  float z = -10000000000000000;
+  // clang-format off
+  std::vector<float> hm = CHW_to_HWC({
+    z, z, z,
+    0, z, z,
+    z, z, z,
+    z, z, z,
+    0, z, z,
+    z, z, z,
+    z, z, z,
+    0, z, z,
+    z, z, z}, 3, 3, 3);
+  // clang-format on
+
+  auto ret_or_error = RefineLandmarksFromHeatMap(
+      vec_to_lms({{0.5, 0.5}, {0.5, 0.5}, {0.5, 0.5}}), hm.data(), {3, 3, 3}, 3,
+      0.6);
+  MP_EXPECT_OK(ret_or_error);
+  EXPECT_THAT(lms_to_vec(*ret_or_error),
+              ElementsAre(Pair(FloatEq(0.5), FloatEq(0.5)),
+                          Pair(FloatEq(0.5), FloatEq(0.5)),
+                          Pair(FloatEq(0.5), FloatEq(0.5))));
+}
+
+TEST(RefineLandmarksFromHeatmapTest, Border) {
+  float z = -10000000000000000;
+  // clang-format off
+  std::vector<float> hm = CHW_to_HWC({
+    z, z, z,
+    0, z, 0,
+    z, z, z,
+
+    z, z, z,
+    0, z, 0,
+    z, z, 0}, 3, 3, 2);
+  // clang-format on
+
+  auto ret_or_error = RefineLandmarksFromHeatMap(
+      vec_to_lms({{0.0, 0.0}, {0.9, 0.9}}), hm.data(), {3, 3, 2}, 3, 0.1);
+  MP_EXPECT_OK(ret_or_error);
+  EXPECT_THAT(lms_to_vec(*ret_or_error),
+              ElementsAre(Pair(FloatEq(0), FloatEq(1 / 3.)),
+                          Pair(FloatEq(2 / 3.), FloatEq(1 / 6. + 2 / 6.))));
+}
+
+}  // namespace
+}  // namespace mediapipe

mediapipe/calculators/util/thresholding_calculator.cc

@@ -101,7 +101,7 @@ absl::Status ThresholdingCalculator::Open(CalculatorContext* cc) {
   }
 
   if (cc->InputSidePackets().HasTag("THRESHOLD")) {
-    threshold_ = cc->InputSidePackets().Tag("THRESHOLD").Get<float>();
+    threshold_ = cc->InputSidePackets().Tag("THRESHOLD").Get<double>();
   }
   return absl::OkStatus();
 }

mediapipe/examples/android/src/java/com/google/mediapipe/apps/holistictrackinggpu/BUILD

@@ -43,8 +43,7 @@ android_binary(
         "//mediapipe/modules/hand_landmark:handedness.txt",
         "//mediapipe/modules/holistic_landmark:hand_recrop.tflite",
         "//mediapipe/modules/pose_detection:pose_detection.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_upper_body.tflite",
+        "//mediapipe/modules/pose_landmark:pose_landmark_full.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_full_body.tflite",
     ],
     assets_dir = "",
     manifest = "//mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:AndroidManifest.xml",

mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu/BUILD

@@ -37,7 +37,7 @@ android_binary(
     srcs = glob(["*.java"]),
     assets = [
         "//mediapipe/graphs/pose_tracking:pose_tracking_gpu.binarypb",
-        "//mediapipe/modules/pose_landmark:pose_landmark_full_body.tflite",
+        "//mediapipe/modules/pose_landmark:pose_landmark_full.tflite",
         "//mediapipe/modules/pose_detection:pose_detection.tflite",
     ],
     assets_dir = "",

mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD

@@ -1,64 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:private"])
-
-cc_binary(
-    name = "libmediapipe_jni.so",
-    linkshared = 1,
-    linkstatic = 1,
-    deps = [
-        "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_gpu_deps",
-        "//mediapipe/java/com/google/mediapipe/framework/jni:mediapipe_framework_jni",
-    ],
-)
-
-cc_library(
-    name = "mediapipe_jni_lib",
-    srcs = [":libmediapipe_jni.so"],
-    alwayslink = 1,
-)
-
-android_binary(
-    name = "upperbodyposetrackinggpu",
-    srcs = glob(["*.java"]),
-    assets = [
-        "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_gpu.binarypb",
-        "//mediapipe/modules/pose_landmark:pose_landmark_upper_body.tflite",
-        "//mediapipe/modules/pose_detection:pose_detection.tflite",
-    ],
-    assets_dir = "",
-    manifest = "//mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:AndroidManifest.xml",
-    manifest_values = {
-        "applicationId": "com.google.mediapipe.apps.upperbodyposetrackinggpu",
-        "appName": "Upper Body Pose Tracking",
-        "mainActivity": ".MainActivity",
-        "cameraFacingFront": "False",
-        "binaryGraphName": "upper_body_pose_tracking_gpu.binarypb",
-        "inputVideoStreamName": "input_video",
-        "outputVideoStreamName": "output_video",
-        "flipFramesVertically": "True",
-        "converterNumBuffers": "2",
-    },
-    multidex = "native",
-    deps = [
-        ":mediapipe_jni_lib",
-        "//mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:basic_lib",
-        "//mediapipe/framework/formats:landmark_java_proto_lite",
-        "//mediapipe/java/com/google/mediapipe/framework:android_framework",
-        "@com_google_protobuf//:protobuf_javalite",
-    ],
-)

mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/MainActivity.java

@@ -1,75 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-package com.google.mediapipe.apps.upperbodyposetrackinggpu;
-
-import android.os.Bundle;
-import android.util.Log;
-import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmark;
-import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList;
-import com.google.mediapipe.framework.PacketGetter;
-import com.google.protobuf.InvalidProtocolBufferException;
-
-/** Main activity of MediaPipe upper-body pose tracking app. */
-public class MainActivity extends com.google.mediapipe.apps.basic.MainActivity {
-  private static final String TAG = "MainActivity";
-
-  private static final String OUTPUT_LANDMARKS_STREAM_NAME = "pose_landmarks";
-
-  @Override
-  protected void onCreate(Bundle savedInstanceState) {
-    super.onCreate(savedInstanceState);
-
-    // To show verbose logging, run:
-    // adb shell setprop log.tag.MainActivity VERBOSE
-    if (Log.isLoggable(TAG, Log.VERBOSE)) {
-      processor.addPacketCallback(
-          OUTPUT_LANDMARKS_STREAM_NAME,
-          (packet) -> {
-            Log.v(TAG, "Received pose landmarks packet.");
-            try {
-              NormalizedLandmarkList poseLandmarks =
-                  PacketGetter.getProto(packet, NormalizedLandmarkList.class);
-              Log.v(
-                  TAG,
-                  "[TS:"
-                      + packet.getTimestamp()
-                      + "] "
-                      + getPoseLandmarksDebugString(poseLandmarks));
-            } catch (InvalidProtocolBufferException exception) {
-              Log.e(TAG, "Failed to get proto.", exception);
-            }
-          });
-    }
-  }
-
-  private static String getPoseLandmarksDebugString(NormalizedLandmarkList poseLandmarks) {
-    String poseLandmarkStr = "Pose landmarks: " + poseLandmarks.getLandmarkCount() + "\n";
-    int landmarkIndex = 0;
-    for (NormalizedLandmark landmark : poseLandmarks.getLandmarkList()) {
-      poseLandmarkStr +=
-          "\tLandmark ["
-              + landmarkIndex
-              + "]: ("
-              + landmark.getX()
-              + ", "
-              + landmark.getY()
-              + ", "
-              + landmark.getZ()
-              + ")\n";
-      ++landmarkIndex;
-    }
-    return poseLandmarkStr;
-  }
-}

mediapipe/examples/coral/graphs/face_detection_desktop_live.pbtxt

@@ -142,26 +142,13 @@ node {
   }
 }
 
-# Maps detection label IDs to the corresponding label text ("Face"). The label
-# map is provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "labeled_detections"
-  options: {
-    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
-      label_map_path: "mediapipe/models/face_detection_front_labelmap.txt"
-    }
-  }
-}
-
 # Adjusts detection locations (already normalized to [0.f, 1.f]) on the
 # letterboxed image (after image transformation with the FIT scale mode) to the
 # corresponding locations on the same image with the letterbox removed (the
 # input image to the graph before image transformation).
 node {
   calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
+  input_stream: "DETECTIONS:filtered_detections"
   input_stream: "LETTERBOX_PADDING:letterbox_padding"
   output_stream: "DETECTIONS:output_detections"
 }

mediapipe/examples/desktop/autoflip/calculators/content_zooming_calculator.cc

@@ -33,6 +33,10 @@ constexpr char kDetections[] = "DETECTIONS";
 constexpr char kDetectedBorders[] = "BORDERS";
 constexpr char kCropRect[] = "CROP_RECT";
 constexpr char kFirstCropRect[] = "FIRST_CROP_RECT";
+// Can be used to control whether an animated zoom should actually performed
+// (configured through option us_to_first_rect). If provided, a non-zero integer
+// will allow the animated zoom to be used when the first detections arrive.
+constexpr char kAnimateZoom[] = "ANIMATE_ZOOM";
 // Field-of-view (degrees) of the camera's x-axis (width).
 // TODO: Parameterize FOV based on camera specs.
 constexpr float kFieldOfView = 60;
@@ -76,10 +80,10 @@ class ContentZoomingCalculator : public CalculatorBase {
   absl::Status InitializeState(int frame_width, int frame_height);
   // Adjusts state to work with an updated frame size.
   absl::Status UpdateForResolutionChange(int frame_width, int frame_height);
-  // Returns true if we are zooming to the initial rect.
+  // Returns true if we are animating to the first rect.
-  bool IsZoomingToInitialRect(const Timestamp& timestamp) const;
+  bool IsAnimatingToFirstRect(const Timestamp& timestamp) const;
-  // Builds the output rectangle when zooming to the initial rect.
+  // Builds the output rectangle when animating to the first rect.
-  absl::StatusOr<mediapipe::Rect> GetInitialZoomingRect(
+  absl::StatusOr<mediapipe::Rect> GetAnimationRect(
       int frame_width, int frame_height, const Timestamp& timestamp) const;
   // Converts bounds to tilt offset, pan offset and height.
   absl::Status ConvertToPanTiltZoom(float xmin, float xmax, float ymin,
@@ -97,7 +101,10 @@ class ContentZoomingCalculator : public CalculatorBase {
   std::unique_ptr<KinematicPathSolver> path_solver_tilt_;
   // Are parameters initialized.
   bool initialized_;
-  // Stores the time of the first crop rectangle.
+  // Stores the time of the first crop rectangle. This is used to control
+  // animating to it. Until a first crop rectangle was computed, it has
+  // the value Timestamp::Unset(). If animating is not requested, it receives
+  // the value Timestamp::Done() instead of the time.
   Timestamp first_rect_timestamp_;
   // Stores the first crop rectangle.
   mediapipe::NormalizedRect first_rect_;
@@ -135,6 +142,9 @@ absl::Status ContentZoomingCalculator::GetContract(
   if (cc->Inputs().HasTag(kDetections)) {
     cc->Inputs().Tag(kDetections).Set<std::vector<mediapipe::Detection>>();
   }
+  if (cc->Inputs().HasTag(kAnimateZoom)) {
+    cc->Inputs().Tag(kAnimateZoom).Set<bool>();
+  }
   if (cc->Outputs().HasTag(kDetectedBorders)) {
     cc->Outputs().Tag(kDetectedBorders).Set<StaticFeatures>();
   }
@@ -419,10 +429,11 @@ absl::Status ContentZoomingCalculator::UpdateForResolutionChange(
   return absl::OkStatus();
 }
 
-bool ContentZoomingCalculator::IsZoomingToInitialRect(
+bool ContentZoomingCalculator::IsAnimatingToFirstRect(
     const Timestamp& timestamp) const {
   if (options_.us_to_first_rect() == 0 ||
-      first_rect_timestamp_ == Timestamp::Unset()) {
+      first_rect_timestamp_ == Timestamp::Unset() ||
+      first_rect_timestamp_ == Timestamp::Done()) {
     return false;
   }
 
@@ -443,10 +454,10 @@ double easeInOutQuad(double t) {
 double lerp(double a, double b, double i) { return a * (1 - i) + b * i; }
 }  // namespace
 
-absl::StatusOr<mediapipe::Rect> ContentZoomingCalculator::GetInitialZoomingRect(
+absl::StatusOr<mediapipe::Rect> ContentZoomingCalculator::GetAnimationRect(
     int frame_width, int frame_height, const Timestamp& timestamp) const {
-  RET_CHECK(IsZoomingToInitialRect(timestamp))
+  RET_CHECK(IsAnimatingToFirstRect(timestamp))
-      << "Must only be called if zooming to initial rect.";
+      << "Must only be called if animating to first rect.";
 
   const int64 delta_us = (timestamp - first_rect_timestamp_).Value();
   const int64 delay = options_.us_to_first_rect_delay();
@@ -538,15 +549,20 @@ absl::Status ContentZoomingCalculator::Process(
     }
   }
 
-  bool zooming_to_initial_rect = IsZoomingToInitialRect(cc->InputTimestamp());
+  const bool may_start_animation = (options_.us_to_first_rect() != 0) &&
+                                   (!cc->Inputs().HasTag(kAnimateZoom) ||
+                                    cc->Inputs().Tag(kAnimateZoom).Get<bool>());
+  bool is_animating = IsAnimatingToFirstRect(cc->InputTimestamp());
 
   int offset_y, height, offset_x;
-  if (zooming_to_initial_rect) {
+  if (!is_animating && options_.start_zoomed_out() && !may_start_animation &&
-    // If we are zooming to the first rect, ignore any new incoming detections.
+      first_rect_timestamp_ == Timestamp::Unset()) {
-    height = last_measured_height_;
+    // If we should start zoomed out and won't be doing an animation,
-    offset_x = last_measured_x_offset_;
+    // initialize the path solvers using the full frame, ignoring detections.
-    offset_y = last_measured_y_offset_;
+    height = max_frame_value_ * frame_height_;
-  } else if (only_required_found) {
+    offset_x = (target_aspect_ * height) / 2;
+    offset_y = frame_height_ / 2;
+  } else if (!is_animating && only_required_found) {
     // Convert bounds to tilt/zoom and in pixel coordinates.
     MP_RETURN_IF_ERROR(ConvertToPanTiltZoom(xmin, xmax, ymin, ymax, &offset_y,
                                             &offset_x, &height));
@@ -555,9 +571,9 @@ absl::Status ContentZoomingCalculator::Process(
     last_measured_height_ = height;
     last_measured_x_offset_ = offset_x;
     last_measured_y_offset_ = offset_y;
-  } else if (cc->InputTimestamp().Microseconds() -
+  } else if (!is_animating && cc->InputTimestamp().Microseconds() -
-                 last_only_required_detection_ >=
+                                      last_only_required_detection_ >=
-             options_.us_before_zoomout()) {
+                                  options_.us_before_zoomout()) {
     // No only_require detections found within salient regions packets
     // arriving since us_before_zoomout duration.
     height = max_frame_value_ * frame_height_ +
@@ -566,7 +582,8 @@ absl::Status ContentZoomingCalculator::Process(
     offset_x = (target_aspect_ * height) / 2;
     offset_y = frame_height_ / 2;
   } else {
-    // No only detection found but using last detection due to
+    // Either animating to the first rectangle, or
+    // no only detection found but using last detection due to
     // duration_before_zoomout_us setting.
     height = last_measured_height_;
     offset_x = last_measured_x_offset_;
@@ -642,24 +659,28 @@ absl::Status ContentZoomingCalculator::Process(
         .AddPacket(Adopt(features.release()).At(cc->InputTimestamp()));
   }
 
-  if (first_rect_timestamp_ == Timestamp::Unset() &&
+  // Record the first crop rectangle
-      options_.us_to_first_rect() != 0) {
+  if (first_rect_timestamp_ == Timestamp::Unset()) {
-    first_rect_timestamp_ = cc->InputTimestamp();
     first_rect_.set_x_center(path_offset_x / static_cast<float>(frame_width_));
     first_rect_.set_width(path_height * target_aspect_ /
                           static_cast<float>(frame_width_));
     first_rect_.set_y_center(path_offset_y / static_cast<float>(frame_height_));
     first_rect_.set_height(path_height / static_cast<float>(frame_height_));
-    // After setting the first rectangle, check whether we should zoom to it.
+
-    zooming_to_initial_rect = IsZoomingToInitialRect(cc->InputTimestamp());
+    // Record the time to serve as departure point for the animation.
+    // If we are not allowed to start the animation, set Timestamp::Done.
+    first_rect_timestamp_ =
+        may_start_animation ? cc->InputTimestamp() : Timestamp::Done();
+    // After setting the first rectangle, check whether we should animate to it.
+    is_animating = IsAnimatingToFirstRect(cc->InputTimestamp());
   }
 
   // Transmit downstream to glcroppingcalculator.
   if (cc->Outputs().HasTag(kCropRect)) {
     std::unique_ptr<mediapipe::Rect> gpu_rect;
-    if (zooming_to_initial_rect) {
+    if (is_animating) {
-      auto rect = GetInitialZoomingRect(frame_width, frame_height,
+      auto rect =
-                                        cc->InputTimestamp());
+          GetAnimationRect(frame_width, frame_height, cc->InputTimestamp());
       MP_RETURN_IF_ERROR(rect.status());
       gpu_rect = absl::make_unique<mediapipe::Rect>(*rect);
     } else {

mediapipe/examples/desktop/autoflip/calculators/content_zooming_calculator.proto

@@ -19,7 +19,7 @@ package mediapipe.autoflip;
 import "mediapipe/examples/desktop/autoflip/quality/kinematic_path_solver.proto";
 import "mediapipe/framework/calculator.proto";
 
-// NextTag: 17
+// NextTag: 18
 message ContentZoomingCalculatorOptions {
   extend mediapipe.CalculatorOptions {
     optional ContentZoomingCalculatorOptions ext = 313091992;
@@ -58,9 +58,15 @@ message ContentZoomingCalculatorOptions {
   // Whether to keep state between frames or to compute the final crop rect.
   optional bool is_stateless = 14 [default = false];
 
-  // Duration (in MicroSeconds) for moving to the first crop rect.
+  // If true, on the first packet start with the camera zoomed out and then zoom
+  // in on the subject.  If false, the camera will start zoomed in on the
+  // subject.
+  optional bool start_zoomed_out = 17 [default = false];
+
+  // Duration (in MicroSeconds) for animating to the first crop rect.
+  // Note that if set, takes precedence over start_zoomed_out.
   optional int64 us_to_first_rect = 15 [default = 0];
-  // Duration (in MicroSeconds) to delay moving to the first crop rect.
+  // Duration (in MicroSeconds) to delay animating to the first crop rect.
   // Used only if us_to_first_rect is set and is interpreted as part of the
   // us_to_first_rect time budget.
   optional int64 us_to_first_rect_delay = 16 [default = 0];

mediapipe/examples/desktop/autoflip/calculators/content_zooming_calculator_test.cc

@@ -127,6 +127,29 @@ const char kConfigD[] = R"(
     }
     )";
 
+const char kConfigE[] = R"(
+    calculator: "ContentZoomingCalculator"
+    input_stream: "VIDEO_SIZE:size"
+    input_stream: "DETECTIONS:detections"
+    input_stream: "ANIMATE_ZOOM:animate_zoom"
+    output_stream: "CROP_RECT:rect"
+    output_stream: "FIRST_CROP_RECT:first_rect"
+    options: {
+      [mediapipe.autoflip.ContentZoomingCalculatorOptions.ext]: {
+        max_zoom_value_deg: 0
+        kinematic_options_zoom {
+          min_motion_to_reframe: 1.2
+        }
+        kinematic_options_tilt {
+          min_motion_to_reframe: 1.2
+        }
+        kinematic_options_pan {
+          min_motion_to_reframe: 1.2
+        }
+      }
+    }
+    )";
+
 void CheckBorder(const StaticFeatures& static_features, int width, int height,
                  int top_border, int bottom_border) {
   ASSERT_EQ(2, static_features.border().size());
@@ -145,9 +168,14 @@ void CheckBorder(const StaticFeatures& static_features, int width, int height,
   EXPECT_EQ(Border::BOTTOM, part.relative_position());
 }
 
+struct AddDetectionFlags {
+  std::optional<bool> animated_zoom;
+};
+
 void AddDetectionFrameSize(const cv::Rect_<float>& position, const int64 time,
                            const int width, const int height,
-                           CalculatorRunner* runner) {
+                           CalculatorRunner* runner,
+                           const AddDetectionFlags& flags = {}) {
   auto detections = std::make_unique<std::vector<mediapipe::Detection>>();
   if (position.width > 0 && position.height > 0) {
     mediapipe::Detection detection;
@@ -175,6 +203,14 @@ void AddDetectionFrameSize(const cv::Rect_<float>& position, const int64 time,
   runner->MutableInputs()
       ->Tag("VIDEO_SIZE")
       .packets.push_back(Adopt(input_size.release()).At(Timestamp(time)));
+
+  if (flags.animated_zoom.has_value()) {
+    runner->MutableInputs()
+        ->Tag("ANIMATE_ZOOM")
+        .packets.push_back(
+            mediapipe::MakePacket<bool>(flags.animated_zoom.value())
+                .At(Timestamp(time)));
+  }
 }
 
 void AddDetection(const cv::Rect_<float>& position, const int64 time,
@@ -703,7 +739,33 @@ TEST(ContentZoomingCalculatorTest, MaxZoomOutValue) {
   CheckCropRect(500, 500, 1000, 1000, 2,
                 runner->Outputs().Tag("CROP_RECT").packets);
 }
+
 TEST(ContentZoomingCalculatorTest, StartZoomedOut) {
+  auto config = ParseTextProtoOrDie<CalculatorGraphConfig::Node>(kConfigD);
+  auto* options = config.mutable_options()->MutableExtension(
+      ContentZoomingCalculatorOptions::ext);
+  options->set_start_zoomed_out(true);
+  auto runner = ::absl::make_unique<CalculatorRunner>(config);
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 0, 1000, 1000,
+                        runner.get());
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 400000, 1000, 1000,
+                        runner.get());
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 800000, 1000, 1000,
+                        runner.get());
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 1000000, 1000, 1000,
+                        runner.get());
+  MP_ASSERT_OK(runner->Run());
+  CheckCropRect(500, 500, 1000, 1000, 0,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 880, 880, 1,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 760, 760, 2,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 655, 655, 3,
+                runner->Outputs().Tag("CROP_RECT").packets);
+}
+
+TEST(ContentZoomingCalculatorTest, AnimateToFirstRect) {
   auto config = ParseTextProtoOrDie<CalculatorGraphConfig::Node>(kConfigD);
   auto* options = config.mutable_options()->MutableExtension(
       ContentZoomingCalculatorOptions::ext);
@@ -733,6 +795,65 @@ TEST(ContentZoomingCalculatorTest, StartZoomedOut) {
                 runner->Outputs().Tag("CROP_RECT").packets);
 }
 
+TEST(ContentZoomingCalculatorTest, CanControlAnimation) {
+  auto config = ParseTextProtoOrDie<CalculatorGraphConfig::Node>(kConfigE);
+  auto* options = config.mutable_options()->MutableExtension(
+      ContentZoomingCalculatorOptions::ext);
+  options->set_start_zoomed_out(true);
+  options->set_us_to_first_rect(1000000);
+  options->set_us_to_first_rect_delay(500000);
+  auto runner = ::absl::make_unique<CalculatorRunner>(config);
+  // Request the animation for the first frame.
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 0, 1000, 1000,
+                        runner.get(), {.animated_zoom = true});
+  // We now stop requesting animated zoom and expect the already started
+  // animation run to completion. This tests that the zoom in continues in the
+  // call when it was started in the Meet greenroom.
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 400000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 800000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 1000000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 1500000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  MP_ASSERT_OK(runner->Run());
+  CheckCropRect(500, 500, 1000, 1000, 0,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 1000, 1000, 1,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 470, 470, 2,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 222, 222, 3,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 222, 222, 4,
+                runner->Outputs().Tag("CROP_RECT").packets);
+}
+
+TEST(ContentZoomingCalculatorTest, DoesNotAnimateIfDisabledViaInput) {
+  auto config = ParseTextProtoOrDie<CalculatorGraphConfig::Node>(kConfigE);
+  auto* options = config.mutable_options()->MutableExtension(
+      ContentZoomingCalculatorOptions::ext);
+  options->set_start_zoomed_out(true);
+  options->set_us_to_first_rect(1000000);
+  options->set_us_to_first_rect_delay(500000);
+  auto runner = ::absl::make_unique<CalculatorRunner>(config);
+  // Disable the animation already for the first frame.
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 0, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 400000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  AddDetectionFrameSize(cv::Rect_<float>(.4, .4, .2, .2), 800000, 1000, 1000,
+                        runner.get(), {.animated_zoom = false});
+  MP_ASSERT_OK(runner->Run());
+  CheckCropRect(500, 500, 1000, 1000, 0,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 880, 880, 1,
+                runner->Outputs().Tag("CROP_RECT").packets);
+  CheckCropRect(500, 500, 760, 760, 2,
+                runner->Outputs().Tag("CROP_RECT").packets);
+}
+
 TEST(ContentZoomingCalculatorTest, ProvidesZeroSizeFirstRectWithoutDetections) {
   auto config = ParseTextProtoOrDie<CalculatorGraphConfig::Node>(kConfigD);
   auto runner = ::absl::make_unique<CalculatorRunner>(config);

mediapipe/examples/desktop/autoflip/calculators/face_box_adjuster_calculator.proto

@@ -47,4 +47,10 @@ message FaceBoxAdjusterCalculatorOptions {
   // and height respectively.
   optional float ipd_face_box_width_ratio = 6 [default = 0.5566];
   optional float ipd_face_box_height_ratio = 7 [default = 0.3131];
+
+  // The max look up angle before considering the eye distance unstable.
+  optional float max_head_tilt_angle_deg = 8 [default = 12.0];
+  // The max amount of time to use an old eye distance when the face look angle
+  // is unstable.
+  optional int32 max_facesize_history_us = 9 [default = 8000000];
 }

mediapipe/examples/desktop/autoflip/calculators/scene_cropping_calculator_test.cc

@@ -345,8 +345,7 @@ TEST(SceneCroppingCalculatorTest, ChecksPriorFrameBufferSize) {
 TEST(SceneCroppingCalculatorTest, ChecksDebugConfigWithoutCroppedFrame) {
   const CalculatorGraphConfig::Node config =
       ParseTextProtoOrDie<CalculatorGraphConfig::Node>(absl::Substitute(
-          kDebugConfigNoCroppedFrame, kTargetWidth, kTargetHeight,
+          kDebugConfigNoCroppedFrame, kTargetWidth, kTargetHeight));
-          kTargetSizeType, 0, kPriorFrameBufferSize));
   auto runner = absl::make_unique<CalculatorRunner>(config);
   const auto status = runner->Run();
   EXPECT_FALSE(status.ok());

mediapipe/examples/desktop/autoflip/quality/kinematic_path_solver.cc

@@ -220,7 +220,7 @@ absl::Status KinematicPathSolver::GetTargetPosition(int* target_position) {
 
 absl::Status KinematicPathSolver::UpdatePixelsPerDegree(
     const float pixels_per_degree) {
-  RET_CHECK_GT(pixels_per_degree_, 0)
+  RET_CHECK_GT(pixels_per_degree, 0)
       << "pixels_per_degree must be larger than 0.";
   pixels_per_degree_ = pixels_per_degree;
   return absl::OkStatus();

mediapipe/examples/desktop/autoflip/subgraph/face_detection_subgraph.pbtxt

@@ -38,7 +38,7 @@ node {
   output_stream: "TENSORS:detection_tensors"
   options: {
     [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/models/face_detection_back.tflite"
+      model_path: "mediapipe/modules/face_detection/face_detection_back.tflite"
     }
   }
 }
@@ -111,26 +111,13 @@ node {
   }
 }
 
-# Maps detection label IDs to the corresponding label text ("Face"). The label
-# map is provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "labeled_detections"
-  options: {
-    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
-      label_map_path: "mediapipe/models/face_detection_back_labelmap.txt"
-    }
-  }
-}
-
 # Adjusts detection locations (already normalized to [0.f, 1.f]) on the
 # letterboxed image (after image transformation with the FIT scale mode) to the
 # corresponding locations on the same image with the letterbox removed (the
 # input image to the graph before image transformation).
 node {
   calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
+  input_stream: "DETECTIONS:filtered_detections"
   input_stream: "LETTERBOX_PADDING:letterbox_padding"
   output_stream: "DETECTIONS:output_detections"
 }

mediapipe/examples/desktop/upper_body_pose_tracking/BUILD

@@ -1,34 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//mediapipe/examples:__subpackages__"])
-
-cc_binary(
-    name = "upper_body_pose_tracking_cpu",
-    deps = [
-        "//mediapipe/examples/desktop:demo_run_graph_main",
-        "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_cpu_deps",
-    ],
-)
-
-# Linux only
-cc_binary(
-    name = "upper_body_pose_tracking_gpu",
-    deps = [
-        "//mediapipe/examples/desktop:demo_run_graph_main_gpu",
-        "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_gpu_deps",
-    ],
-)

mediapipe/examples/ios/holistictrackinggpu/BUILD

@@ -61,8 +61,7 @@ objc_library(
         "//mediapipe/modules/hand_landmark:handedness.txt",
         "//mediapipe/modules/holistic_landmark:hand_recrop.tflite",
         "//mediapipe/modules/pose_detection:pose_detection.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_full_body.tflite",
+        "//mediapipe/modules/pose_landmark:pose_landmark_full.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_upper_body.tflite",
     ],
     deps = [
         "//mediapipe/examples/ios/common:CommonMediaPipeAppLibrary",

mediapipe/examples/ios/posetrackinggpu/BUILD

@@ -63,7 +63,7 @@ objc_library(
     data = [
         "//mediapipe/graphs/pose_tracking:pose_tracking_gpu.binarypb",
         "//mediapipe/modules/pose_detection:pose_detection.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_full_body.tflite",
+        "//mediapipe/modules/pose_landmark:pose_landmark_full.tflite",
     ],
     deps = [
         "//mediapipe/examples/ios/common:CommonMediaPipeAppLibrary",

mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD

@@ -1,78 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "@build_bazel_rules_apple//apple:ios.bzl",
-    "ios_application",
-)
-load(
-    "//mediapipe/examples/ios:bundle_id.bzl",
-    "BUNDLE_ID_PREFIX",
-    "example_provisioning",
-)
-
-licenses(["notice"])
-
-MIN_IOS_VERSION = "10.0"
-
-alias(
-    name = "upperbodyposetrackinggpu",
-    actual = "UpperBodyPoseTrackingGpuApp",
-)
-
-ios_application(
-    name = "UpperBodyPoseTrackingGpuApp",
-    app_icons = ["//mediapipe/examples/ios/common:AppIcon"],
-    bundle_id = BUNDLE_ID_PREFIX + ".UpperBodyPoseTrackingGpu",
-    families = [
-        "iphone",
-        "ipad",
-    ],
-    infoplists = [
-        "//mediapipe/examples/ios/common:Info.plist",
-        "Info.plist",
-    ],
-    minimum_os_version = MIN_IOS_VERSION,
-    provisioning_profile = example_provisioning(),
-    deps = [
-        ":UpperBodyPoseTrackingGpuAppLibrary",
-        "@ios_opencv//:OpencvFramework",
-    ],
-)
-
-objc_library(
-    name = "UpperBodyPoseTrackingGpuAppLibrary",
-    srcs = [
-        "UpperBodyPoseTrackingViewController.mm",
-    ],
-    hdrs = [
-        "UpperBodyPoseTrackingViewController.h",
-    ],
-    copts = ["-std=c++17"],
-    data = [
-        "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_gpu.binarypb",
-        "//mediapipe/modules/pose_detection:pose_detection.tflite",
-        "//mediapipe/modules/pose_landmark:pose_landmark_upper_body.tflite",
-    ],
-    deps = [
-        "//mediapipe/examples/ios/common:CommonMediaPipeAppLibrary",
-    ] + select({
-        "//mediapipe:ios_i386": [],
-        "//mediapipe:ios_x86_64": [],
-        "//conditions:default": [
-            "//mediapipe/graphs/pose_tracking:upper_body_pose_tracking_gpu_deps",
-            "//mediapipe/framework/formats:landmark_cc_proto",
-        ],
-    }),
-)

mediapipe/examples/ios/upperbodyposetrackinggpu/Info.plist

@@ -1,16 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-  <key>CameraPosition</key>
-  <string>back</string>
-  <key>MainViewController</key>
-  <string>UpperBodyPoseTrackingViewController</string>
-  <key>GraphOutputStream</key>
-  <string>output_video</string>
-  <key>GraphInputStream</key>
-  <string>input_video</string>
-  <key>GraphName</key>
-  <string>upper_body_pose_tracking_gpu</string>
-</dict>
-</plist>

mediapipe/examples/ios/upperbodyposetrackinggpu/UpperBodyPoseTrackingViewController.mm

@@ -1,53 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#import "UpperBodyPoseTrackingViewController.h"
-
-#include "mediapipe/framework/formats/landmark.pb.h"
-
-static const char* kLandmarksOutputStream = "pose_landmarks";
-
-@implementation UpperBodyPoseTrackingViewController
-
-#pragma mark - UIViewController methods
-
-- (void)viewDidLoad {
-  [super viewDidLoad];
-
-  [self.mediapipeGraph addFrameOutputStream:kLandmarksOutputStream
-                           outputPacketType:MPPPacketTypeRaw];
-}
-
-#pragma mark - MPPGraphDelegate methods
-
-// Receives a raw packet from the MediaPipe graph. Invoked on a MediaPipe worker thread.
-- (void)mediapipeGraph:(MPPGraph*)graph
-     didOutputPacket:(const ::mediapipe::Packet&)packet
-          fromStream:(const std::string&)streamName {
-  if (streamName == kLandmarksOutputStream) {
-    if (packet.IsEmpty()) {
-      NSLog(@"[TS:%lld] No pose landmarks", packet.Timestamp().Value());
-      return;
-    }
-    const auto& landmarks = packet.Get<::mediapipe::NormalizedLandmarkList>();
-    NSLog(@"[TS:%lld] Number of pose landmarks: %d", packet.Timestamp().Value(),
-          landmarks.landmark_size());
-    for (int i = 0; i < landmarks.landmark_size(); ++i) {
-      NSLog(@"\tLandmark[%d]: (%f, %f, %f)", i, landmarks.landmark(i).x(),
-            landmarks.landmark(i).y(), landmarks.landmark(i).z());
-    }
-  }
-}
-
-@end

mediapipe/framework/BUILD

@@ -15,6 +15,7 @@
 #
 
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
+load("@bazel_skylib//:bzl_library.bzl", "bzl_library")
 
 licenses(["notice"])
 
@@ -27,10 +28,28 @@ package_group(
     ],
 )
 
-exports_files([
+bzl_library(
-    "transitive_protos.bzl",
+    name = "transitive_protos_bzl",
-    "encode_binary_proto.bzl",
+    srcs = [
-])
+        "transitive_protos.bzl",
+    ],
+    visibility = ["//mediapipe/framework:__subpackages__"],
+)
+
+bzl_library(
+    name = "encode_binary_proto_bzl",
+    srcs = [
+        "encode_binary_proto.bzl",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+alias(
+    name = "encode_binary_proto",
+    actual = ":encode_binary_proto_bzl",
+    deprecation = "Use encode_binary_proto_bzl",
+    visibility = ["//visibility:public"],
+)
 
 mediapipe_proto_library(
     name = "calculator_proto",

mediapipe/framework/api2/BUILD

@@ -1,15 +1,8 @@
 package(
-    default_visibility = [":preview_users"],
+    default_visibility = ["//visibility:public"],
     features = ["-use_header_modules"],
 )
 
-package_group(
-    name = "preview_users",
-    packages = [
-        "//mediapipe/...",
-    ],
-)
-
 licenses(["notice"])
 
 cc_library(

mediapipe/framework/calculator.proto

@@ -422,6 +422,9 @@ message CalculatorGraphConfig {
   // the graph config.
   string type = 20;
 
-  // Can be used for annotating a graph.
+  // The types and default values for graph options, in proto2 syntax.
   MediaPipeOptions options = 1001;
+
+  // The types and default values for graph options, in proto3 syntax.
+  repeated google.protobuf.Any graph_options = 1002;
 }

mediapipe/framework/calculator_graph.cc

@@ -411,7 +411,8 @@ absl::Status CalculatorGraph::Initialize(
 
 absl::Status CalculatorGraph::ObserveOutputStream(
     const std::string& stream_name,
-    std::function<absl::Status(const Packet&)> packet_callback) {
+    std::function<absl::Status(const Packet&)> packet_callback,
+    bool observe_timestamp_bounds) {
   RET_CHECK(initialized_).SetNoLogging()
       << "CalculatorGraph is not initialized.";
   // TODO Allow output observers to be attached by graph level
@@ -425,7 +426,7 @@ absl::Status CalculatorGraph::ObserveOutputStream(
   auto observer = absl::make_unique<internal::OutputStreamObserver>();
   MP_RETURN_IF_ERROR(observer->Initialize(
       stream_name, &any_packet_type_, std::move(packet_callback),
-      &output_stream_managers_[output_stream_index]));
+      &output_stream_managers_[output_stream_index], observe_timestamp_bounds));
   graph_output_streams_.push_back(std::move(observer));
   return absl::OkStatus();
 }

mediapipe/framework/calculator_graph.h

@@ -157,7 +157,8 @@ class CalculatorGraph {
   // TODO: Rename to AddOutputStreamCallback.
   absl::Status ObserveOutputStream(
       const std::string& stream_name,
-      std::function<absl::Status(const Packet&)> packet_callback);
+      std::function<absl::Status(const Packet&)> packet_callback,
+      bool observe_timestamp_bounds = false);
 
   // Adds an OutputStreamPoller for a stream. This provides a synchronous,
   // polling API for accessing a stream's output. Should only be called before

mediapipe/framework/calculator_graph_bounds_test.cc

@@ -1518,5 +1518,72 @@ TEST(CalculatorGraphBoundsTest, OffsetAndBound) {
   MP_ASSERT_OK(graph.WaitUntilDone());
 }
 
+// A Calculator that sends empty output stream packets.
+class EmptyPacketCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Inputs().Index(0).Set<int>();
+    cc->Outputs().Index(0).SetSameAs(&cc->Inputs().Index(0));
+    return absl::OkStatus();
+  }
+  absl::Status Open(CalculatorContext* cc) final { return absl::OkStatus(); }
+  absl::Status Process(CalculatorContext* cc) final {
+    if (cc->InputTimestamp().Value() % 2 == 0) {
+      cc->Outputs().Index(0).AddPacket(Packet().At(cc->InputTimestamp()));
+    }
+    return absl::OkStatus();
+  }
+};
+REGISTER_CALCULATOR(EmptyPacketCalculator);
+
+// This test shows that an output timestamp bound can be specified by outputing
+// an empty packet with a settled timestamp.
+TEST(CalculatorGraphBoundsTest, EmptyPacketOutput) {
+  // OffsetAndBoundCalculator runs on parallel threads and sends ts
+  // occasionally.
+  std::string config_str = R"(
+            input_stream: "input_0"
+            node {
+              calculator: "EmptyPacketCalculator"
+              input_stream: "input_0"
+              output_stream: "empty_0"
+            }
+            node {
+              calculator: "ProcessBoundToPacketCalculator"
+              input_stream: "empty_0"
+              output_stream: "output_0"
+            }
+          )";
+  CalculatorGraphConfig config =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(config_str);
+  CalculatorGraph graph;
+  std::vector<Packet> output_0_packets;
+  MP_ASSERT_OK(graph.Initialize(config));
+  MP_ASSERT_OK(graph.ObserveOutputStream("output_0", [&](const Packet& p) {
+    output_0_packets.push_back(p);
+    return absl::OkStatus();
+  }));
+  MP_ASSERT_OK(graph.StartRun({}));
+  MP_ASSERT_OK(graph.WaitUntilIdle());
+
+  // Send in packets.
+  for (int i = 0; i < 9; ++i) {
+    const int ts = 10 + i * 10;
+    Packet p = MakePacket<int>(i).At(Timestamp(ts));
+    MP_ASSERT_OK(graph.AddPacketToInputStream("input_0", p));
+    MP_ASSERT_OK(graph.WaitUntilIdle());
+  }
+
+  // 9 empty packets are converted to bounds and then to packets.
+  EXPECT_EQ(output_0_packets.size(), 9);
+  for (int i = 0; i < 9; ++i) {
+    EXPECT_EQ(output_0_packets[i].Timestamp(), Timestamp(10 + i * 10));
+  }
+
+  // Shutdown the graph.
+  MP_ASSERT_OK(graph.CloseAllPacketSources());
+  MP_ASSERT_OK(graph.WaitUntilDone());
+}
+
 }  // namespace
 }  // namespace mediapipe

mediapipe/framework/deps/BUILD

@@ -16,11 +16,20 @@
 #   The dependencies of mediapipe.
 
 load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
+load("@bazel_skylib//:bzl_library.bzl", "bzl_library")
 
 licenses(["notice"])
 
 package(default_visibility = ["//visibility:private"])
 
+bzl_library(
+    name = "expand_template_bzl",
+    srcs = [
+        "expand_template.bzl",
+    ],
+    visibility = ["//mediapipe/framework:__subpackages__"],
+)
+
 proto_library(
     name = "proto_descriptor_proto",
     srcs = ["proto_descriptor.proto"],

mediapipe/framework/formats/BUILD

@@ -295,6 +295,7 @@ cc_library(
         "//mediapipe/framework/formats:image_format_cc_proto",
         "@com_google_absl//absl/synchronization",
         "//mediapipe/framework:port",
+        "//mediapipe/framework:type_map",
         "//mediapipe/framework/port:logging",
     ] + select({
         "//conditions:default": [

mediapipe/framework/formats/image.cc

@@ -14,6 +14,8 @@
 
 #include "mediapipe/framework/formats/image.h"
 
+#include "mediapipe/framework/type_map.h"
+
 namespace mediapipe {
 
 // TODO Refactor common code from GpuBufferToImageFrameCalculator
@@ -67,8 +69,7 @@ bool Image::ConvertToGpu() const {
 #else
   if (use_gpu_) return true;  // Already on GPU.
 #if MEDIAPIPE_GPU_BUFFER_USE_CV_PIXEL_BUFFER
-  auto packet = MakePacket<ImageFrame>(std::move(*image_frame_));
+  auto packet = PointToForeign<ImageFrame>(image_frame_.get());
-  image_frame_ = nullptr;
   CFHolder<CVPixelBufferRef> buffer;
   auto status = CreateCVPixelBufferForImageFramePacket(packet, true, &buffer);
   CHECK_OK(status);
@@ -94,4 +95,7 @@ bool Image::ConvertToGpu() const {
 #endif  // MEDIAPIPE_DISABLE_GPU
 }
 
+MEDIAPIPE_REGISTER_TYPE(mediapipe::Image, "::mediapipe::Image", nullptr,
+                        nullptr);
+
 }  // namespace mediapipe

mediapipe/framework/formats/image.h

@@ -72,8 +72,8 @@ class Image {
 
   // Creates an Image representing the same image content as the ImageFrame
   // the input shared pointer points to, and retaining shared ownership.
-  explicit Image(ImageFrameSharedPtr frame_buffer)
+  explicit Image(ImageFrameSharedPtr image_frame)
-      : image_frame_(std::move(frame_buffer)) {
+      : image_frame_(std::move(image_frame)) {
     use_gpu_ = false;
     pixel_mutex_ = std::make_shared<absl::Mutex>();
   }

mediapipe/framework/formats/tensor.cc

@@ -30,6 +30,9 @@
 
 namespace mediapipe {
 
+// Zero and negative values are not checked here.
+bool IsPowerOfTwo(int v) { return (v & (v - 1)) == 0; }
+
 int BhwcBatchFromShape(const Tensor::Shape& shape) {
   LOG_IF(FATAL, shape.dims.empty())
       << "Tensor::Shape must be non-empty to retrieve a named dimension";
@@ -237,6 +240,12 @@ void Tensor::AllocateOpenGlTexture2d() const {
       glTexStorage2D(GL_TEXTURE_2D, 1, GL_RGBA32F, texture_width_,
                      texture_height_);
     } else {
+      // GLES2.0 supports only clamp addressing mode for NPOT textures.
+      // If any of dimensions is NPOT then both addressing modes are clamp.
+      if (!IsPowerOfTwo(texture_width_) || !IsPowerOfTwo(texture_height_)) {
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE);
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE);
+      }
       // We assume all contexts will have the same extensions, so we only check
       // once for OES_texture_float extension, to save time.
       static bool has_oes_extension =

mediapipe/framework/graph_output_stream.cc

@@ -14,13 +14,16 @@
 
 #include "mediapipe/framework/graph_output_stream.h"
 
+#include "absl/synchronization/mutex.h"
+#include "mediapipe/framework/port/status.h"
+
 namespace mediapipe {
 
 namespace internal {
 
 absl::Status GraphOutputStream::Initialize(
     const std::string& stream_name, const PacketType* packet_type,
-    OutputStreamManager* output_stream_manager) {
+    OutputStreamManager* output_stream_manager, bool observe_timestamp_bounds) {
   RET_CHECK(output_stream_manager);
 
   // Initializes input_stream_handler_ with one input stream as the observer.
@@ -31,6 +34,7 @@ absl::Status GraphOutputStream::Initialize(
   input_stream_handler_ = absl::make_unique<GraphOutputStreamHandler>(
       tag_map, /*cc_manager=*/nullptr, MediaPipeOptions(),
       /*calculator_run_in_parallel=*/false);
+  input_stream_handler_->SetProcessTimestampBounds(observe_timestamp_bounds);
   const CollectionItemId& id = tag_map->BeginId();
   input_stream_ = absl::make_unique<InputStreamManager>();
   MP_RETURN_IF_ERROR(
@@ -52,20 +56,58 @@ void GraphOutputStream::PrepareForRun(
 absl::Status OutputStreamObserver::Initialize(
     const std::string& stream_name, const PacketType* packet_type,
     std::function<absl::Status(const Packet&)> packet_callback,
-    OutputStreamManager* output_stream_manager) {
+    OutputStreamManager* output_stream_manager, bool observe_timestamp_bounds) {
   RET_CHECK(output_stream_manager);
 
   packet_callback_ = std::move(packet_callback);
+  observe_timestamp_bounds_ = observe_timestamp_bounds;
   return GraphOutputStream::Initialize(stream_name, packet_type,
-                                       output_stream_manager);
+                                       output_stream_manager,
+                                       observe_timestamp_bounds);
 }
 
 absl::Status OutputStreamObserver::Notify() {
+  // Lets one thread perform packets notification as much as possible.
+  // Other threads should quit if a thread is already performing notification.
+  {
+    absl::MutexLock l(&mutex_);
+
+    if (notifying_ == false) {
+      notifying_ = true;
+    } else {
+      return absl::OkStatus();
+    }
+  }
   while (true) {
     bool empty;
     Timestamp min_timestamp = input_stream_->MinTimestampOrBound(&empty);
     if (empty) {
-      break;
+      // Emits an empty packet at timestamp_bound.PreviousAllowedInStream().
+      if (observe_timestamp_bounds_ && min_timestamp < Timestamp::Done()) {
+        Timestamp settled = (min_timestamp == Timestamp::PostStream()
+                                 ? Timestamp::PostStream()
+                                 : min_timestamp.PreviousAllowedInStream());
+        if (last_processed_ts_ < settled) {
+          MP_RETURN_IF_ERROR(packet_callback_(Packet().At(settled)));
+          last_processed_ts_ = settled;
+        }
+      }
+      // Last check to make sure that the min timestamp or bound doesn't change.
+      // If so, flips notifying_ to false to allow any other threads to perform
+      // notification when new packets/timestamp bounds arrive. Otherwise, in
+      // case of the min timestamp or bound getting updated, jumps to the
+      // beginning of the notification loop for a new iteration.
+      {
+        absl::MutexLock l(&mutex_);
+        Timestamp new_min_timestamp =
+            input_stream_->MinTimestampOrBound(&empty);
+        if (new_min_timestamp == min_timestamp) {
+          notifying_ = false;
+          break;
+        } else {
+          continue;
+        }
+      }
     }
     int num_packets_dropped = 0;
     bool stream_is_done = false;
@@ -75,6 +117,7 @@ absl::Status OutputStreamObserver::Notify() {
         << absl::Substitute("Dropped $0 packet(s) on input stream \"$1\".",
                             num_packets_dropped, input_stream_->Name());
     MP_RETURN_IF_ERROR(packet_callback_(packet));
+    last_processed_ts_ = min_timestamp;
   }
   return absl::OkStatus();
 }

mediapipe/framework/graph_output_stream.h

@@ -52,7 +52,8 @@ class GraphOutputStream {
   // is not transferred to the graph output stream object.
   absl::Status Initialize(const std::string& stream_name,
                           const PacketType* packet_type,
-                          OutputStreamManager* output_stream_manager);
+                          OutputStreamManager* output_stream_manager,
+                          bool observe_timestamp_bounds = false);
 
   // Installs callbacks into its GraphOutputStreamHandler.
   virtual void PrepareForRun(std::function<void()> notification_callback,
@@ -99,6 +100,10 @@ class GraphOutputStream {
     }
   };
 
+  bool observe_timestamp_bounds_;
+  absl::Mutex mutex_;
+  bool notifying_ ABSL_GUARDED_BY(mutex_) = false;
+  Timestamp last_processed_ts_ = Timestamp::Unstarted();
   std::unique_ptr<InputStreamHandler> input_stream_handler_;
   std::unique_ptr<InputStreamManager> input_stream_;
 };
@@ -112,7 +117,8 @@ class OutputStreamObserver : public GraphOutputStream {
   absl::Status Initialize(
       const std::string& stream_name, const PacketType* packet_type,
       std::function<absl::Status(const Packet&)> packet_callback,
-      OutputStreamManager* output_stream_manager);
+      OutputStreamManager* output_stream_manager,
+      bool observe_timestamp_bounds = false);
 
   // Notifies the observer of new packets emitted by the observed
   // output stream.
@@ -128,6 +134,7 @@ class OutputStreamObserver : public GraphOutputStream {
 
 // OutputStreamPollerImpl that returns packets to the caller via
 // Next()/NextBatch().
+// TODO: Support observe_timestamp_bounds.
 class OutputStreamPollerImpl : public GraphOutputStream {
  public:
   virtual ~OutputStreamPollerImpl() {}

mediapipe/framework/mediapipe_options.proto

@@ -20,6 +20,9 @@ syntax = "proto2";
 
 package mediapipe;
 
+option java_package = "com.google.mediapipe.proto";
+option java_outer_classname = "MediaPipeOptionsProto";
+
 // Options used by a MediaPipe object.
 message MediaPipeOptions {
   extensions 20000 to max;

mediapipe/framework/output_stream_shard.cc

@@ -101,8 +101,8 @@ Status OutputStreamShard::AddPacketInternal(T&& packet) {
   }
 
   if (packet.IsEmpty()) {
-    return mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
+    SetNextTimestampBound(packet.Timestamp().NextAllowedInStream());
-           << "Empty packet sent to stream \"" << Name() << "\".";
+    return absl::OkStatus();
   }
 
   const Timestamp timestamp = packet.Timestamp();

mediapipe/framework/tool/BUILD

@@ -20,6 +20,7 @@ load(
     "mediapipe_binary_graph",
 )
 load("//mediapipe/framework:mediapipe_cc_test.bzl", "mediapipe_cc_test")
+load("@bazel_skylib//:bzl_library.bzl", "bzl_library")
 
 licenses(["notice"])
 
@@ -29,6 +30,30 @@ exports_files([
     "simple_subgraph_template.cc",
 ])
 
+bzl_library(
+    name = "mediapipe_graph_bzl",
+    srcs = [
+        "mediapipe_graph.bzl",
+    ],
+    visibility = ["//visibility:public"],
+    deps = [
+        ":build_defs_bzl",
+        "//mediapipe/framework:encode_binary_proto",
+        "//mediapipe/framework:transitive_protos_bzl",
+        "//mediapipe/framework/deps:expand_template_bzl",
+    ],
+)
+
+bzl_library(
+    name = "build_defs_bzl",
+    srcs = [
+        "build_defs.bzl",
+    ],
+    visibility = [
+        "//mediapipe/framework:__subpackages__",
+    ],
+)
+
 cc_library(
     name = "text_to_binary_graph",
     srcs = ["text_to_binary_graph.cc"],
@@ -744,5 +769,7 @@ cc_test(
 
 exports_files(
     ["build_defs.bzl"],
-    visibility = ["//mediapipe/framework:__subpackages__"],
+    visibility = [
+        "//mediapipe/framework:__subpackages__",
+    ],
 )

mediapipe/framework/tool/sink.cc

@@ -19,6 +19,7 @@
 #include "mediapipe/framework/tool/sink.h"
 
 #include <memory>
+#include <utility>
 #include <vector>
 
 #include "absl/strings/str_cat.h"
@@ -168,8 +169,19 @@ void AddMultiStreamCallback(
     std::function<void(const std::vector<Packet>&)> callback,
     CalculatorGraphConfig* config,
     std::pair<std::string, Packet>* side_packet) {
+  std::map<std::string, Packet> side_packets;
+  AddMultiStreamCallback(streams, callback, config, &side_packets,
+                         /*observe_timestamp_bounds=*/false);
+  *side_packet = *side_packets.begin();
+}
+
+void AddMultiStreamCallback(
+    const std::vector<std::string>& streams,
+    std::function<void(const std::vector<Packet>&)> callback,
+    CalculatorGraphConfig* config, std::map<std::string, Packet>* side_packets,
+    bool observe_timestamp_bounds) {
   CHECK(config);
-  CHECK(side_packet);
+  CHECK(side_packets);
   CalculatorGraphConfig::Node* sink_node = config->add_node();
   const std::string name = GetUnusedNodeName(
       *config, absl::StrCat("multi_callback_", absl::StrJoin(streams, "_")));
@@ -179,15 +191,23 @@ void AddMultiStreamCallback(
     sink_node->add_input_stream(stream_name);
   }
 
+  if (observe_timestamp_bounds) {
+    const std::string observe_ts_bounds_packet_name = GetUnusedSidePacketName(
+        *config, absl::StrCat(name, "_observe_ts_bounds"));
+    sink_node->add_input_side_packet(absl::StrCat(
+        "OBSERVE_TIMESTAMP_BOUNDS:", observe_ts_bounds_packet_name));
+    InsertIfNotPresent(side_packets, observe_ts_bounds_packet_name,
+                       MakePacket<bool>(true));
+  }
   const std::string input_side_packet_name =
       GetUnusedSidePacketName(*config, absl::StrCat(name, "_callback"));
-  side_packet->first = input_side_packet_name;
   sink_node->add_input_side_packet(
       absl::StrCat("VECTOR_CALLBACK:", input_side_packet_name));
 
-  side_packet->second =
+  InsertIfNotPresent(
+      side_packets, input_side_packet_name,
       MakePacket<std::function<void(const std::vector<Packet>&)>>(
-          std::move(callback));
+          std::move(callback)));
 }
 
 void AddCallbackWithHeaderCalculator(const std::string& stream_name,
@@ -240,6 +260,10 @@ absl::Status CallbackCalculator::GetContract(CalculatorContract* cc) {
     return mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
            << "InputSidePackets must use tags.";
   }
+  if (cc->InputSidePackets().HasTag("OBSERVE_TIMESTAMP_BOUNDS")) {
+    cc->InputSidePackets().Tag("OBSERVE_TIMESTAMP_BOUNDS").Set<bool>();
+    cc->SetProcessTimestampBounds(true);
+  }
 
   int count = allow_multiple_streams ? cc->Inputs().NumEntries("") : 1;
   for (int i = 0; i < count; ++i) {
@@ -266,6 +290,12 @@ absl::Status CallbackCalculator::Open(CalculatorContext* cc) {
     return mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
            << "missing callback.";
   }
+  if (cc->InputSidePackets().HasTag("OBSERVE_TIMESTAMP_BOUNDS") &&
+      !cc->InputSidePackets().Tag("OBSERVE_TIMESTAMP_BOUNDS").Get<bool>()) {
+    return mediapipe::InvalidArgumentErrorBuilder(MEDIAPIPE_LOC)
+           << "The value of the OBSERVE_TIMESTAMP_BOUNDS input side packet "
+              "must be set to true";
+  }
   return absl::OkStatus();
 }
 

mediapipe/framework/tool/sink.h

@@ -115,6 +115,12 @@ void AddMultiStreamCallback(
     std::function<void(const std::vector<Packet>&)> callback,
     CalculatorGraphConfig* config, std::pair<std::string, Packet>* side_packet);
 
+void AddMultiStreamCallback(
+    const std::vector<std::string>& streams,
+    std::function<void(const std::vector<Packet>&)> callback,
+    CalculatorGraphConfig* config, std::map<std::string, Packet>* side_packets,
+    bool observe_timestamp_bounds = false);
+
 // Add a CallbackWithHeaderCalculator to intercept packets sent on
 // stream stream_name, and the header packet on stream stream_header.
 // The input side packet with the produced name callback_side_packet_name

mediapipe/framework/tool/sink_test.cc

@@ -146,5 +146,63 @@ TEST(CallbackTest, TestAddMultiStreamCallback) {
   EXPECT_THAT(sums, testing::ElementsAre(15, 7, 9));
 }
 
+class TimestampBoundTestCalculator : public CalculatorBase {
+ public:
+  static absl::Status GetContract(CalculatorContract* cc) {
+    cc->Outputs().Index(0).Set<int>();
+    cc->Outputs().Index(1).Set<int>();
+    return absl::OkStatus();
+  }
+  absl::Status Open(CalculatorContext* cc) final { return absl::OkStatus(); }
+  absl::Status Process(CalculatorContext* cc) final {
+    if (count_ % 5 == 0) {
+      cc->Outputs().Index(0).SetNextTimestampBound(Timestamp(count_ + 1));
+      cc->Outputs().Index(1).SetNextTimestampBound(Timestamp(count_ + 1));
+    }
+    ++count_;
+    if (count_ == 13) {
+      return tool::StatusStop();
+    }
+    return absl::OkStatus();
+  }
+
+ private:
+  int count_ = 1;
+};
+REGISTER_CALCULATOR(TimestampBoundTestCalculator);
+
+TEST(CallbackTest, TestAddMultiStreamCallbackWithTimestampNotification) {
+  std::string config_str = R"(
+            node {
+              calculator: "TimestampBoundTestCalculator"
+              output_stream: "foo"
+              output_stream: "bar"
+            }
+          )";
+  CalculatorGraphConfig graph_config =
+      mediapipe::ParseTextProtoOrDie<CalculatorGraphConfig>(config_str);
+
+  std::vector<int> sums;
+
+  std::map<std::string, Packet> side_packets;
+  tool::AddMultiStreamCallback(
+      {"foo", "bar"},
+      [&sums](const std::vector<Packet>& packets) {
+        Packet foo_p = packets[0];
+        Packet bar_p = packets[1];
+        ASSERT_TRUE(foo_p.IsEmpty() && bar_p.IsEmpty());
+        int foo = foo_p.Timestamp().Value();
+        int bar = bar_p.Timestamp().Value();
+        sums.push_back(foo + bar);
+      },
+      &graph_config, &side_packets, true);
+
+  CalculatorGraph graph(graph_config);
+  MP_ASSERT_OK(graph.StartRun(side_packets));
+  MP_ASSERT_OK(graph.WaitUntilDone());
+
+  EXPECT_THAT(sums, testing::ElementsAre(10, 20));
+}
+
 }  // namespace
 }  // namespace mediapipe

mediapipe/gpu/BUILD

@@ -14,7 +14,7 @@
 
 load("//mediapipe/gpu:metal.bzl", "metal_library")
 load("@build_bazel_rules_apple//apple:ios.bzl", "ios_unit_test")
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
+load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library", "mediapipe_proto_library")
 load("//mediapipe/framework:mediapipe_cc_test.bzl", "mediapipe_cc_test")
 
 licenses(["notice"])
@@ -240,6 +240,12 @@ cc_library(
     ],
 )
 
+mediapipe_proto_library(
+    name = "gpu_origin_proto",
+    srcs = ["gpu_origin.proto"],
+    visibility = ["//visibility:public"],
+)
+
 objc_library(
     name = "pixel_buffer_pool_util",
     srcs = ["pixel_buffer_pool_util.mm"],
@@ -460,6 +466,8 @@ cc_library(
         "//mediapipe/framework:calculator_context",
         "//mediapipe/framework:calculator_node",
         "//mediapipe/framework/port:logging",
+        "//mediapipe/util:resource_cache",
+        "@com_google_absl//absl/hash",
         "@com_google_absl//absl/memory",
         "@com_google_absl//absl/synchronization",
     ] + select({
@@ -760,8 +768,10 @@ cc_library(
     deps = [
         ":gl_calculator_helper",
         ":gl_quad_renderer",
+        ":gpu_buffer",
         ":shader_util",
         "//mediapipe/framework:calculator_framework",
+        "//mediapipe/framework/api2:node",
         "//mediapipe/framework/port:ret_check",
         "//mediapipe/framework/port:status",
         "//mediapipe/gpu:gl_surface_sink_calculator_cc_proto",

mediapipe/gpu/gl_context.cc

@@ -563,8 +563,14 @@ class GlFenceSyncPoint : public GlSyncPoint {
 
   void WaitOnGpu() override {
     if (!sync_) return;
-    // TODO: do not wait if we are already on the same context?
+      // TODO: do not wait if we are already on the same context?
+      // WebGL2 specifies a waitSync call, but since cross-context
+      // synchronization is not supported, it's actually a no-op. Firefox prints
+      // a warning when it's called, so let's just skip the call. See
+      // b/184637485 for details.
+#ifndef __EMSCRIPTEN__
     glWaitSync(sync_, 0, GL_TIMEOUT_IGNORED);
+#endif
   }
 
   bool IsReady() override {