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
-_TENSORFLOW_GIT_COMMIT = "c67f68021824410ebe9f18513b8856ac1c6d4887"
-_TENSORFLOW_SHA256= "fd07d0b39422dc435e268c5e53b2646a8b4b1e3151b87837b43f86068faae87f"
+# 2021-04-30
+_TENSORFLOW_GIT_COMMIT = "5bd3c57ef184543d22e34e36cff9d9bea608e06d"
+_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

@@ -37,34 +37,37 @@ 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"],
     )
     ```
 
 2.  Run the Bazel build command to generate the AAR.
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
-        --fat_apk_cpu=arm64-v8a,armeabi-v7a --strip=ALWAYS \
-        //path/to/the/aar/build/file:aar_name
+    bazel build -c opt --strip=ALWAYS \
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //path/to/the/aar/build/file:aar_name.aar
     ```
 
-    For the face detection AAR target we made in the step 1, run:
+    For the face detection AAR target we made in step 1, run:
 
     ```bash
-    bazel build -c opt --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --fat_apk_cpu=arm64-v8a,armeabi-v7a \
-        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar
+    bazel build -c opt --strip=ALWAYS \
+        --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
+        --fat_apk_cpu=arm64-v8a,armeabi-v7a \
+        //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar
 
     # It should print:
-    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mp_face_detection_aar up-to-date:
-    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    # Target //mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example:mediapipe_face_detection.aar up-to-date:
+    # bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     ```
 
 3.  (Optional) Save the AAR to your preferred location.
 
     ```bash
-    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mp_face_detection_aar.aar
+    cp bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/aar_example/mediapipe_face_detection.aar
     /absolute/path/to/your/preferred/location
     ```
 
@@ -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
-    cp bazel-bin/mediapipe/graphs/face_detection/mobile_gpu.binarypb /path/to/your/app/src/main/assets/
+    bazel build -c opt mediapipe/graphs/face_detection:face_detection_mobile_gpu_binary_graph
+    cp bazel-bin/mediapipe/graphs/face_detection/face_detection_mobile_gpu.binarypb /path/to/your/app/src/main/assets/
     cp mediapipe/modules/face_detection/face_detection_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
-    app/src/main/jniLibs.
-
-    MediaPipe depends on OpenCV, you will need to copy the precompiled OpenCV so
-    files into app/src/main/jniLibs. You can download the official OpenCV
-    Android SDK from
-    [here](https://github.com/opencv/opencv/releases/download/3.4.3/opencv-3.4.3-android-sdk.zip)
-    and run:
-
-    ```bash
-    cp -R ~/Downloads/OpenCV-android-sdk/sdk/native/libs/arm* /path/to/your/app/src/main/jniLibs/
-    ```
-
-    ![Screenshot](../images/mobile/android_studio_opencv_location.png)
-
-5.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
+4.  Modify app/build.gradle to add MediaPipe dependencies and MediaPipe AAR.
 
     ```
     dependencies {
@@ -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
-(535 in total) instead of the full set of 33 pose landmarks (543 in total). Note
-that upper-body-only prediction may be more accurate for use cases where the
-lower-body parts are mostly out of view. Default to `false`.
+Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
+well as inference latency generally go up with the model complexity. Default to
+`1`.
 
 #### smooth_landmarks
 
@@ -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:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full_body.tflite)
-*   Upper-body pose landmark model:
-    [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body.tflite)
+*   Pose landmark model:
+    [TFLite model (lite)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite),
+    [TFLite model (full)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_full.tflite),
+    [TFLite model (heavy)](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite)
 *   [Model card](https://mediapipe.page.link/blazepose-mc)
 
 ### [Holistic](https://google.github.io/mediapipe/solutions/holistic)