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GitOrigin-RevId: f4b1fe3f15810450fb6539e733f6a260d3ee082c

docs/getting_started/hello_world_ios.md

@@ -113,6 +113,10 @@ bazel to build the iOS application. The content of the
 5.  `Main.storyboard` and `Launch.storyboard`
 6.  `Assets.xcassets` directory.
 
+Note: In newer versions of Xcode, you may see additional files `SceneDelegate.h`
+and `SceneDelegate.m`. Make sure to copy them too and add them to the `BUILD`
+file mentioned below.
+
 Copy these files to a directory named `HelloWorld` to a location that can access
 the MediaPipe source code. For example, the source code of the application that
 we will build in this tutorial is located in
@@ -247,6 +251,12 @@ We need to get frames from the `_cameraSource` into our application
 `MPPInputSourceDelegate`. So our application `ViewController` can be a delegate
 of `_cameraSource`.
 
+Update the interface definition of `ViewController` accordingly:
+
+```
+@interface ViewController () <MPPInputSourceDelegate>
+```
+
 To handle camera setup and process incoming frames, we should use a queue
 different from the main queue. Add the following to the implementation block of
 the `ViewController`:
@@ -288,6 +298,12 @@ utility called `MPPLayerRenderer` to display images on the screen. This utility
 can be used to display `CVPixelBufferRef` objects, which is the type of the
 images provided by `MPPCameraInputSource` to its delegates.
 
+In `ViewController.m`, add the following import line:
+
+```
+#import "mediapipe/objc/MPPLayerRenderer.h"
+```
+
 To display images of the screen, we need to add a new `UIView` object called
 `_liveView` to the `ViewController`.
 
@@ -411,6 +427,12 @@ Objective-C++.
 
 ### Use the graph in `ViewController`
 
+In `ViewController.m`, add the following import line:
+
+```
+#import "mediapipe/objc/MPPGraph.h"
+```
+
 Declare a static constant with the name of the graph, the input stream and the
 output stream:
 
@@ -549,6 +571,12 @@ method to receive packets on this output stream and display them on the screen:
 }
 ```
 
+Update the interface definition of `ViewController` with `MPPGraphDelegate`:
+
+```
+@interface ViewController () <MPPGraphDelegate, MPPInputSourceDelegate>
+```
+
 And that is all! Build and run the app on your iOS device. You should see the
 results of running the edge detection graph on a live video feed. Congrats!
 
@@ -560,5 +588,5 @@ appropriate `BUILD` file dependencies for the edge detection graph.
 
 [Bazel]:https://bazel.build/
 [`edge_detection_mobile_gpu.pbtxt`]:https://github.com/google/mediapipe/tree/master/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
-[common]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common)
-[helloworld]:(https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld)
+[common]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/common
+[helloworld]:https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/helloworld

docs/getting_started/install.md

@@ -796,7 +796,7 @@ This will use a Docker image that will isolate mediapipe's installation from the
     ```bash
     $ docker run -it --name mediapipe mediapipe:latest
 
-    root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazel run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
+    root@bca08b91ff63:/mediapipe# GLOG_logtostderr=1 bazelisk run --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/hello_world:hello_world
 
     # Should print:
     # Hello World!

docs/solutions/objectron.md

@@ -529,7 +529,7 @@ Example app bounding boxes are rendered with [GlAnimationOverlayCalculator](http
 > ```
 > and then run
 >
-> ```build
+> ```bash
 > bazel run -c opt mediapipe/graphs/object_detection_3d/obj_parser:ObjParser -- input_dir=[INTERMEDIATE_OUTPUT_DIR] output_dir=[OUTPUT_DIR]
 > ```
 > INPUT_DIR should be the folder with initial asset .obj files to be processed,

docs/solutions/pose.md

@@ -141,7 +141,7 @@ Optionally, MediaPipe Pose can predicts a full-body
 Please find more detail in the
 [BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html),
 this [paper](https://arxiv.org/abs/2006.10204),
-[the model card](./models.md#pose) and the [Output](#Output) section below.
+[the model card](./models.md#pose) and the [Output](#output) section below.
 
 ## Solution APIs
 
@@ -281,8 +281,8 @@ with mp_pose.Pose(
       continue
     print(
         f'Nose coordinates: ('
-        f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
-        f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
+        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
     )
 
     annotated_image = image.copy()
@@ -369,6 +369,7 @@ Supported configuration options:
   <div class="container">
     <video class="input_video"></video>
     <canvas class="output_canvas" width="1280px" height="720px"></canvas>
+    <div class="landmark-grid-container"></div>
   </div>
 </body>
 </html>

docs/solutions/selfie_segmentation.md

@@ -262,7 +262,7 @@ to visualize its associated subgraphs, please see
     [(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1DoeyGzMmWUsjfVgZfGGecrn7GKzYcEAo/view?usp=sharing)
     [`mediapipe/examples/android/src/java/com/google/mediapipe/apps/selfiesegmentationgpu:selfiesegmentationgpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/selfiesegmentationgpu/BUILD)
 *   iOS target:
-    [`mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp`](http:/mediapipe/examples/ios/selfiesegmentationgpu/BUILD)
+    [`mediapipe/examples/ios/selfiesegmentationgpu:SelfieSegmentationGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/selfiesegmentationgpu/BUILD)
 
 ### Desktop
 

docs/solutions/solutions.md

@@ -13,6 +13,9 @@ has_toc: false
 {:toc}
 ---
 
+MediaPipe offers open source cross-platform, customizable ML solutions for live
+and streaming media.
+
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