Project import generated by Copybara.

PiperOrigin-RevId: 264105834

Author: MediaPipe Team

Diff for: mediapipe/docs/face_detection_mobile_gpu.md

@@ -8,33 +8,24 @@ that performs face detection with TensorFlow Lite on GPU.
 
 ## Android
 
-Please see [Hello World! in MediaPipe on Android](hello_world_android.md) for
-general instructions to develop an Android application that uses MediaPipe.
+[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu)
 
-The graph below is used in the
-[Face Detection GPU Android example app](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu).
-To build the app, run:
+To build and install the app:
 
 ```bash
 bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu
-```
-
-To further install the app on an Android device, run:
-
-```bash
 adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/facedetectiongpu/facedetectiongpu.apk
 ```
 
 ## iOS
 
-Please see [Hello World! in MediaPipe on iOS](hello_world_ios.md) for general
-instructions to develop an iOS application that uses MediaPipe.
+[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectiongpu).
+
+See the general [instructions](./mediapipe_ios_setup.md) for building iOS
+examples and generating an Xcode project. This will be the FaceDetectionGpuApp
+target.
 
-The graph below is used in the
-[Face Detection GPU iOS example app](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/facedetectiongpu).
-To build the app, please see the general
-[MediaPipe iOS app building and setup instructions](./mediapipe_ios_setup.md).
-Specific to this example, run:
+To build on the command line:
 
 ```bash
 bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/facedetectiongpu:FaceDetectionGpuApp
@@ -51,7 +42,7 @@ below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs face detection with TensorFlow Lite on GPU.
-# Used in the example in
+# Used in the examples in
 # mediapipie/examples/android/src/java/com/mediapipe/apps/facedetectiongpu and
 # mediapipie/examples/ios/facedetectiongpu.
 
@@ -227,9 +218,7 @@ node {
   }
 }
 
-# Draws annotations and overlays them on top of a GPU copy of the original
-# image coming into the graph. The calculator assumes that image origin is
-# always at the top-left corner and renders text accordingly.
+# Draws annotations and overlays them on top of the input images.
 node {
   calculator: "AnnotationOverlayCalculator"
   input_stream: "INPUT_FRAME_GPU:throttled_input_video"

Diff for: mediapipe/docs/hair_segmentation_mobile_gpu.md

@@ -8,20 +8,12 @@ that performs hair segmentation with TensorFlow Lite on GPU.
 
 ## Android
 
-Please see [Hello World! in MediaPipe on Android](hello_world_android.md) for
-general instructions to develop an Android application that uses MediaPipe.
+[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/hairsegmentationgpu)
 
-The graph below is used in the
-[Hair Segmentation GPU Android example app](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/hairsegmentationgpu).
-To build the app, run:
+To build and install the app:
 
 ```bash
 bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/hairsegmentationgpu
-```
-
-To further install the app on an Android device, run:
-
-```bash
 adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/hairsegmentationgpu/hairsegmentationgpu.apk
 ```
 
@@ -37,7 +29,7 @@ below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 ```bash
 # MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.
 # Used in the example in
-# mediapipie/examples/ios/hairsegmentationgpu.
+# mediapipie/examples/android/src/java/com/mediapipe/apps/hairsegmentationgpu.
 
 # Images on GPU coming into and out of the graph.
 input_stream: "input_video"
@@ -84,14 +76,11 @@ node: {
   }
 }
 
-# Waits for a mask from the previous round of hair segmentation to be fed back
-# as an input, and caches it. Upon the arrival of an input image, it checks if
-# there is a mask cached, and sends out the mask with the timestamp replaced by
-# that of the input image. This is needed so that the "current image" and the
-# "previous mask" share the same timestamp, and as a result can be synchronized
-# and combined in the subsequent calculator. Note that upon the arrival of the
-# very first input frame, an empty packet is sent out to jump start the feedback
-# loop.
+# Caches a mask fed back from the previous round of hair segmentation, and upon
+# the arrival of the next input image sends out the cached mask with the
+# timestamp replaced by that of the input image, essentially generating a packet
+# that carries the previous mask. Note that upon the arrival of the very first
+# input image, an empty packet is sent out to jump start the feedback loop.
 node {
   calculator: "PreviousLoopbackCalculator"
   input_stream: "MAIN:throttled_input_video"
@@ -114,9 +103,9 @@ node {
 
 # Converts the transformed input image on GPU into an image tensor stored in
 # tflite::gpu::GlBuffer. The zero_center option is set to false to normalize the
-# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f].
-# With the max_num_channels option set to 4, all 4 RGBA channels are contained
-# in the image tensor.
+# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
+# max_num_channels option set to 4, all 4 RGBA channels are contained in the
+# image tensor.
 node {
   calculator: "TfLiteConverterCalculator"
   input_stream: "IMAGE_GPU:mask_embedded_input_video"
@@ -147,7 +136,7 @@ node {
 node {
   calculator: "TfLiteInferenceCalculator"
   input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS:segmentation_tensor"
+  output_stream: "TENSORS_GPU:segmentation_tensor"
   input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
   node_options: {
     [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
@@ -157,23 +146,15 @@ node {
   }
 }
 
-# The next step (tensors to segmentation) is not yet supported on iOS GPU.
-# Convert the previous segmentation mask to CPU for processing.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "previous_hair_mask"
-  output_stream: "previous_hair_mask_cpu"
-}
-
 # Decodes the segmentation tensor generated by the TensorFlow Lite model into a
-# mask of values in [0.f, 1.f], stored in the R channel of a CPU buffer. It also
+# mask of values in [0.f, 1.f], stored in the R channel of a GPU buffer. It also
 # takes the mask generated previously as another input to improve the temporal
 # consistency.
 node {
   calculator: "TfLiteTensorsToSegmentationCalculator"
-  input_stream: "TENSORS:segmentation_tensor"
-  input_stream: "PREV_MASK:previous_hair_mask_cpu"
-  output_stream: "MASK:hair_mask_cpu"
+  input_stream: "TENSORS_GPU:segmentation_tensor"
+  input_stream: "PREV_MASK_GPU:previous_hair_mask"
+  output_stream: "MASK_GPU:hair_mask"
   node_options: {
     [type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
       tensor_width: 512
@@ -185,13 +166,6 @@ node {
   }
 }
 
-# Send the current segmentation mask to GPU for the last step, blending.
-node: {
-  calculator: "ImageFrameToGpuBufferCalculator"
-  input_stream: "hair_mask_cpu"
-  output_stream: "hair_mask"
-}
-
 # Colors the hair segmentation with the color specified in the option.
 node {
   calculator: "RecolorCalculator"

Diff for: mediapipe/docs/hand_detection_mobile_gpu.md

@@ -20,33 +20,32 @@ confidence score to generate the hand rectangle, to be further utilized in the
 
 ## Android
 
-Please see [Hello World! in MediaPipe on Android](hello_world_android.md) for
-general instructions to develop an Android application that uses MediaPipe.
+[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handdetectiongpu)
 
-The graph below is used in the
-[Hand Detection GPU Android example app](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handdetectiongpu).
-To build the app, run:
+An arm64 APK can be
+[downloaded here](https://drive.google.com/open?id=1qUlTtH7Ydg-wl_H6VVL8vueu2UCTu37E).
+
+To build the app yourself:
 
 ```bash
 bazel build -c opt --config=android_arm64 mediapipe/examples/android/src/java/com/google/mediapipe/apps/handdetectiongpu
 ```
 
-To further install the app on an Android device, run:
+Once the app is built, install it on Android device with:
 
 ```bash
 adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/apps/handdetectiongpu/handdetectiongpu.apk
 ```
 
 ## iOS
 
-Please see [Hello World! in MediaPipe on iOS](hello_world_ios.md) for general
-instructions to develop an iOS application that uses MediaPipe.
+[Source](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handdetectiongpu).
+
+See the general [instructions](./mediapipe_ios_setup.md) for building iOS
+examples and generating an Xcode project. This will be the HandDetectionGpuApp
+target.
 
-The graph below is used in the
-[Hand Detection GPU iOS example app](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/handdetectiongpu).
-To build the app, please see the general
-[MediaPipe iOS app building and setup instructions](./mediapipe_ios_setup.md).
-Specific to this example, run:
+To build on the command line:
 
 ```bash
 bazel build -c opt --config=ios_arm64 mediapipe/examples/ios/handdetectiongpu:HandDetectionGpuApp
@@ -70,14 +69,24 @@ Visualizing Subgraphs section in the
 
 ```bash
 # MediaPipe graph that performs hand detection with TensorFlow Lite on GPU.
-# Used in the example in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/handdetectiongpu.
+# Used in the examples in
+# mediapipie/examples/android/src/java/com/mediapipe/apps/handdetectiongpu and
 # mediapipie/examples/ios/handdetectiongpu.
 
 # Images coming into and out of the graph.
 input_stream: "input_video"
 output_stream: "output_video"
 
+# Throttles the images flowing downstream for flow control. It passes through
+# the very first incoming image unaltered, and waits for HandDetectionSubgraph
+# downstream in the graph to finish its tasks before it passes through another
+# image. All images that come in while waiting are dropped, limiting the number
+# of in-flight images in HandDetectionSubgraph to 1. This prevents the nodes in
+# HandDetectionSubgraph from queuing up incoming images and data excessively,
+# which leads to increased latency and memory usage, unwanted in real-time
+# mobile applications. It also eliminates unnecessarily computation, e.g., the
+# output produced by a node in the subgraph may get dropped downstream if the
+# subsequent nodes are still busy processing previous inputs.
 node {
   calculator: "FlowLimiterCalculator"
   input_stream: "input_video"
@@ -89,6 +98,7 @@ node {
   output_stream: "throttled_input_video"
 }
 
+# Subgraph that detections hands (see hand_detection_gpu.pbtxt).
 node {
   calculator: "HandDetectionSubgraph"
   input_stream: "throttled_input_video"
@@ -123,7 +133,7 @@ node {
   }
 }
 
-# Draws annotations and overlays them on top of the input image into the graph.
+# Draws annotations and overlays them on top of the input images.
 node {
   calculator: "AnnotationOverlayCalculator"
   input_stream: "INPUT_FRAME_GPU:throttled_input_video"
@@ -271,8 +281,8 @@ node {
   }
 }
 
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
+# Maps detection label IDs to the corresponding label text ("Palm"). The label
+# map is provided in the label_map_path option.
 node {
   calculator: "DetectionLabelIdToTextCalculator"
   input_stream: "filtered_detections"