Project import generated by Copybara.

PiperOrigin-RevId: 254856010

mediapipe/docs/face_detection_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![face_detection_android_gpu_graph](images/mobile/face_detection_android_gpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on GPU.

mediapipe/docs/hair_segmentation_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![hair_segmentation_android_gpu_graph](images/mobile/hair_segmentation_android_gpu.png){width="600"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.

mediapipe/docs/hello_world_desktop.md

@@ -49,7 +49,7 @@
     ```
 
     You can visualize this graph using
-    [MediaPipe Visualizer](https://mediapipe-viz.appspot.com) by pasting the
+    [MediaPipe Visualizer](https://viz.mediapipe.dev) by pasting the
     CalculatorGraphConfig content below into the visualizer. See
     [here](./visualizer.md) for help on the visualizer.
 

mediapipe/docs/index.rst

@@ -31,6 +31,10 @@ APIs for MediaPipe
     * Graph Execution API in Java (Android)
     * (Coming Soon) Graph Execution API in Objective-C (iOS)
 
+Alpha Disclaimer
+==================
+MediaPipe is currently in alpha for v0.5. We are still making breaking API changes and expect to get to stable API by v1.0. We recommend that you target a specific version of MediaPipe, and periodically bump to the latest release. That way you have control over when a breaking change affects you.
+
 User Documentation
 ==================
 

mediapipe/docs/install.md

@@ -23,7 +23,7 @@ Required libraries
 
 *   Android SDK release 28.0.3 and above
 
-*   Android NDK r18b and above
+*   Android NDK r17c and above
 
 ### Installing on Debian and Ubuntu
 

mediapipe/docs/object_detection_android_cpu.md

@@ -37,7 +37,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![object_detection_android_cpu_graph](images/mobile/object_detection_android_cpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on CPU.

mediapipe/docs/object_detection_android_gpu.md

@@ -29,7 +29,7 @@ adb install bazel-bin/mediapipe/examples/android/src/java/com/google/mediapipe/a
 ![object_detection_android_gpu_graph](images/mobile/object_detection_android_gpu.png){width="400"}
 
 To visualize the graph as shown above, copy the text specification of the graph
-below and paste it into [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/).
+below and paste it into [MediaPipe Visualizer](https://viz.mediapipe.dev/).
 
 ```bash
 # MediaPipe graph that performs object detection with TensorFlow Lite on GPU.

mediapipe/docs/object_detection_desktop.md

@@ -8,7 +8,14 @@ interested in running the same TensorfFlow Lite model on Android, please see the
 [Object Detection on GPU on Android](object_detection_android_gpu.md) and
 [Object Detection on CPU on Android](object_detection_android_cpu.md) examples.
 
-### TensorFlow Model
+We show the object detection demo with both TensorFlow model and TensorFlow Lite model:
+
+-  [TensorFlow Object Detection Demo](#tensorflow-object-detection-demo)
+-  [TensorFlow Lite Object Detection Demo](#tensorflow-lite-object-detection-demo)
+
+Note: If MediaPipe depends on OpenCV 2, please see the [known issues with OpenCV 2](#known-issues-with-opencv-2) section.
+
+### TensorFlow Object Detection Demo
 
 To build and run the TensorFlow example on desktop, run:
 
@@ -40,7 +47,7 @@ $ bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tensorf
 
 To visualize the graph as shown above, copy the text specification of the graph
 below and paste it into
-[MediaPipe Visualizer](https://mediapipe-viz.appspot.com).
+[MediaPipe Visualizer](https://viz.mediapipe.dev).
 
 ```bash
 # MediaPipe graph that performs object detection on desktop with TensorFlow
@@ -176,7 +183,7 @@ node {
 }
 ```
 
-### TensorFlow Lite Model
+### TensorFlow Lite Object Detection Demo
 
 To build and run the TensorFlow Lite example on desktop, run:
 
@@ -204,7 +211,7 @@ $ bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite
 
 To visualize the graph as shown above, copy the text specification of the graph
 below and paste it into
-[MediaPipe Visualizer](https://mediapipe-viz.appspot.com).
+[MediaPipe Visualizer](https://viz.mediapipe.dev).
 
 ```bash
 # MediaPipe graph that performs object detection on desktop with TensorFlow Lite

mediapipe/docs/visualizer.md

@@ -5,7 +5,7 @@
 
 To help users understand the structure of their calculator graphs and to
 understand the overall behavior of their machine learning inference pipelines,
-we have built the [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/) that is available online.
+we have built the [MediaPipe Visualizer](https://viz.mediapipe.dev/) that is available online.
 
 *   A graph view allows users to see a connected calculator graph as expressed
     through a graph configuration that is pasted into the graph editor or