The MediaPipe Face Landmarker task lets you detect face landmarks and facial expressions in images and videos. You can use this task to identify human facial expressions, apply facial filters and effects, and create virtual avatars. This task uses machine learning (ML) models that can work with single images, videos or a continuous stream of images. The task outputs 3-dimensional face landmarks, blendshape scores (coefficients representing facial expression) to infer detailed facial surfaces in real-time, and transformation matrices to perform the transformations required for effects rendering.
The code sample described in these instructions is available on GitHub . You can see this task in action by viewing this Web demo . For more information about the capabilities, models, and configuration options of this task, see the Overview .
Code example
The MediaPipe Tasks example code is a basic implementation of a Face Landmarker app for iOS. The example uses the camera on a physical iOS device to detect face landmarks in a continuous video stream. The app can also detect face landmarks in images and videos from the device gallery.
You can use the app as a starting point for your own iOS app, or refer to it when modifying an existing app. The Face Landmarker example code is hosted on GitHub .
Download the code
The following instructions show you how to create a local copy of the example code using the git command line tool.
To download the example code:
-
Clone the git repository using the following command:
git clone https://github.com/google-ai-edge/mediapipe-samples -
Optionally, configure your git instance to use sparse checkout, so you have only the files for the Face Landmarker example app:
cd mediapipe-samples git sparse-checkout init --cone git sparse-checkout set examples/face_landmarker/ios
After creating a local version of the example code, you can install the MediaPipe task library, open the project using Xcode and run the app. For instructions, see the Setup Guide for iOS .
Key components
The following files contain the crucial code for the Face Landmarker example application:
- FaceLandmarkerService.swift : Initializes the Face Landmarker, handles the model selection, and runs inference on the input data.
- CameraViewController.swift : Implements the UI for the live camera feed input mode and visualizes the results.
- MediaLibraryViewController.swift : Implements the UI for the still image and video file input modes and visualizes the results.
Setup
This section describes key steps for setting up your development environment and code projects to use Face Landmarker. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for iOS .
Dependencies
Face Landmarker uses the MediaPipeTasksVision
library, which must be installed
using CocoaPods. The library is compatible with both Swift and Objective-C apps
and does not require any additional language-specific setup.
For instructions to install CocoaPods on MacOS, refer to the CocoaPods
installation guide
.
For instructions on how to create a Podfile
with the necessary pods for your
app, refer to Using
CocoaPods
.
Add the MediaPipeTasksVision
pod in the Podfile
using the following code:
target
'MyFaceLandmarkerApp'
do
use_frameworks!
pod
'MediaPipeTasksVision'
end
If your app includes unit test targets, refer to the Set Up Guide for
iOS
for additional information on setting
up your Podfile
.
Model
The MediaPipe Face Landmarker task requires a trained model bundle that is compatible with this task. For more information about the available trained models for Face Landmarker, see the task overview Models section .
Select and download a model, and add it to your project directory using Xcode. For instructions on how to add files to your Xcode project, refer to Managing files and folders in your Xcode project .
Use the BaseOptions.modelAssetPath
property to specify the path to the model
in your app bundle. For a code example, see the next section.
Create the task
You can create the Face Landmarker task by calling one of its initializers. The FaceLandmarker(options:)
initializer accepts values for the configuration
options.
If you don't need a Face Landmarker initialized with customized configuration
options, you can use the FaceLandmarker(modelPath:)
initializer to create a
Face Landmarker with the default options. For more information about configuration
options, see the Configuration Overview
.
The Face Landmarker task supports 3 input data types: still images, video files
and live video streams. By default, FaceLandmarker(modelPath:)
initializes a
task for still images. If you want your task to be initialized to process video
files or live video streams, use FaceLandmarker(options:)
to specify the video
or livestream running mode. The livestream mode also requires the additional faceLandmarkerLiveStreamDelegate
configuration option, which enables the
Face Landmarker to deliver face landmarker results to the delegate asynchronously.
Choose the tab corresponding to your running mode to see how to create the task and run inference.
Swift
Image
import MediaPipeTasksVision let modelPath = Bundle . main . path ( forResource : "face_landmarker" , ofType : "task" ) let options = FaceLandmarkerOptions () options . baseOptions . modelAssetPath = modelPath options . runningMode = . image options . minFaceDetectionConfidence = minFaceDetectionConfidence options . minFacePresenceConfidence = minFacePresenceConfidence options . minTrackingConfidence = minTrackingConfidence options . numFaces = numFaces let faceLandmarker = try FaceLandmarker ( options : options )
Video
import MediaPipeTasksVision let modelPath = Bundle . main . path ( forResource : "face_landmarker" , ofType : "task" ) let options = FaceLandmarkerOptions () options . baseOptions . modelAssetPath = modelPath options . runningMode = . video options . minFaceDetectionConfidence = minFaceDetectionConfidence options . minFacePresenceConfidence = minFacePresenceConfidence options . minTrackingConfidence = minTrackingConfidence options . numFaces = numFaces let faceLandmarker = try FaceLandmarker ( options : options )
Livestream
import MediaPipeTasksVision // Class that conforms to the ` FaceLandmarkerLiveStreamDelegate ` protocol and // implements the method that the face landmarker calls once it finishes // performing face landmark detection in each input frame . class FaceLandmarkerResultProcessor : NSObject , FaceLandmarkerLiveStreamDelegate { func faceLandmarker ( _ faceLandmarker : FaceLandmarker , didFinishDetection result : FaceLandmarkerResult ? , timestampInMilliseconds : Int , error : Error ? ) { // Process the face landmarker result or errors here . } } let modelPath = Bundle . main . path ( forResource : "face_landmarker" , ofType : "task" ) let options = FaceLandmarkerOptions () options . baseOptions . modelAssetPath = modelPath options . runningMode = . liveStream options . minFaceDetectionConfidence = minFaceDetectionConfidence options . minFacePresenceConfidence = minFacePresenceConfidence options . minTrackingConfidence = minTrackingConfidence options . numFaces = numFaces // Assign an object of the class to the ` faceLandmarkerLiveStreamDelegate ` // property . let processor = FaceLandmarkerResultProcessor () options . faceLandmarkerLiveStreamDelegate = processor let faceLandmarker = try FaceLandmarker ( options : options )
Objective-C
Image
@import MediaPipeTasksVision ; NSString * modelPath = [[ NSBundle mainBundle ] pathForResource : @ "face_landmarker" ofType : @ "task" ]; MPPFaceLandmarkerOptions * options = [[ MPPFaceLandmarkerOptions alloc ] init ]; options . baseOptions . modelAssetPath = modelPath ; options . runningMode = MPPRunningModeImage ; options . minFaceDetectionConfidence = minFaceDetectionConfidence ; options . minFacePresenceConfidence = minFacePresenceConfidence ; options . minTrackingConfidence = minTrackingConfidence ; options . numFaces = numFaces ; MPPFaceLandmarker * faceLandmarker = [[ MPPFaceLandmarker alloc ] initWithOptions : options error : nil ];
Video
@import MediaPipeTasksVision ; NSString * modelPath = [[ NSBundle mainBundle ] pathForResource : @ "face_landmarker" ofType : @ "task" ]; MPPFaceLandmarkerOptions * options = [[ MPPFaceLandmarkerOptions alloc ] init ]; options . baseOptions . modelAssetPath = modelPath ; options . runningMode = MPPRunningModeVideo ; options . minFaceDetectionConfidence = minFaceDetectionConfidence ; options . minFacePresenceConfidence = minFacePresenceConfidence ; options . minTrackingConfidence = minTrackingConfidence ; options . numFaces = numFaces ; MPPFaceLandmarker * faceLandmarker = [[ MPPFaceLandmarker alloc ] initWithOptions : options error : nil ];
Livestream
@import MediaPipeTasksVision ; // Class that conforms to the `MPPFaceLandmarkerLiveStreamDelegate` protocol // and implements the method that the face landmarker calls once it finishes // performing face landmark detection in each input frame. @interface APPFaceLandmarkerResultProcessor : NSObject@end @implementation APPFaceLandmarkerResultProcessor - ( void ) faceLandmarker: ( MPPFaceLandmarker * ) faceLandmarker didFinishDetectionWithResult :( MPPFaceLandmarkerResult * ) faceLandmarkerResult timestampInMilliseconds :( NSInteger ) timestampInMilliseconds error :( NSError * ) error { // Process the face landmarker result or errors here. } @end NSString * modelPath = [[ NSBundle mainBundle ] pathForResource : @"face_landmarker" ofType : @"task" ]; MPPFaceLandmarkerOptions * options = [[ MPPFaceLandmarkerOptions alloc ] init ]; options . baseOptions . modelAssetPath = modelPath ; options . runningMode = MPPRunningModeLiveStream ; options . minFaceDetectionConfidence = minFaceDetectionConfidence ; options . minFacePresenceConfidence = minFacePresenceConfidence ; options . minTrackingConfidence = minTrackingConfidence ; options . numFaces = numFaces ; // Assign an object of the class to the `faceLandmarkerLiveStreamDelegate` // property. APPFaceLandmarkerResultProcessor * processor = [ APPFaceLandmarkerResultProcessor new ]; options . faceLandmarkerLiveStreamDelegate = processor ; MPPFaceLandmarker * faceLandmarker = [[ MPPFaceLandmarker alloc ] initWithOptions : options error : nil ];
Note:If you use the video mode or livestream mode, Face Landmarker uses tracking to avoid triggering the detection model on every frame, which helps reduce latency.
Configuration options
This task has the following configuration options for iOS apps:
| Option name | Description | Value Range | Default Value |
|---|---|---|---|
runningMode
|
Sets the running mode for the task. Face Landmarker has three modes: IMAGE: The mode for single image inputs. VIDEO: The mode for decoded frames of a video. LIVE_STREAM: The mode for a livestream of input data, such as from a camera. In this mode, `faceLandmarkerLiveStreamDelegate` must be set to an instance of a class that implements the `FaceLandmarkerLiveStreamDelegate` to receive the results of performing face landmark detection asynchronously. |
{RunningMode.image, RunningMode.video, RunningMode.liveStream} | {RunningMode.image} |
numFaces
|
The maximum number of faces that can be detected by the Face Landmarker. Smoothing is only applied when numFaces is set to 1. | Integer > 0 | 1 |
minFaceDetectionConfidence
|
The minimum confidence score for the face detection to be considered successful. | Float [0.0,1.0] | 0.5 |
minFacePresenceConfidence
|
The minimum confidence score of face presence score in the face landmark detection. | Float [0.0,1.0] | 0.5 |
minTrackingConfidence
|
The minimum confidence score for the face tracking to be considered successful. | Float [0.0,1.0] | 0.5 |
outputFaceBlendshapes
|
Whether FaceLandmarker outputs face blendshapes. Face blendshapes are used for rendering the 3D face model. | Bool | false |
outputFacialTransformationMatrixes
|
Whether FaceLandmarker outputs the facial transformation matrix. FaceLandmarker uses the matrix to transform the face landmarks from a canonical face model to the detected face, so users can apply effects on the detected landmarks. | Bool | false |
When the running mode is set to LIVE_STREAM
, the Face Landmarker requires the
additional faceLandmarkerLiveStreamDelegate
configuration option, which
enables the Face Landmarker to deliver face landmark detection results
asynchronously. The delegate must implement the faceLandmarker(_:didFinishDetection:timestampInMilliseconds:error:)
method,
which the Face Landmarker calls after processing the results of performing face
landmark detection on each frame.
| Option name | Description | Value Range | Default Value |
|---|---|---|---|
faceLandmarkerLiveStreamDelegate
|
Enables Face Landmarker to receive the results of performing face
landmark detection asynchronously in livestream mode. The class whose
instance is set to this property must implement the faceLandmarker(_:didFinishDetection:timestampInMilliseconds:error:)
method. |
Not applicable | Not set |
Prepare data
You need to convert the input image or frame to an MPImage
object before
passing it to the Face Landmarker. MPImage
supports different types of iOS image
formats, and can use them in any running mode for inference. For more
information about MPImage
, refer to the MPImage API
.
Choose an iOS image format based on your use case and the running mode your
application requires. MPImage
accepts the UIImage
, CVPixelBuffer
, and CMSampleBuffer
iOS image formats.
UIImage
The UIImage
format is well-suited for the following running modes:
-
Images: images from an app bundle, user gallery, or file system formatted as
UIImageimages can be converted to anMPImageobject. -
Videos: use AVAssetImageGenerator to extract video frames to the CGImage format, then convert them to
UIImageimages.
Swift
// Load an image on the user's device as an iOS `UIImage` object. // Convert the `UIImage` object to a MediaPipe's Image object having the default // orientation `UIImage.Orientation.up`. let image = try MPImage ( uiImage : image )
Objective-C
// Load an image on the user's device as an iOS `UIImage` object. // Convert the `UIImage` object to a MediaPipe's Image object having the default // orientation `UIImageOrientationUp`. MPImage * image = [[ MPPImage alloc ] initWithUIImage : image error : nil ];
The example initializes an MPImage
with the default UIImage.Orientation.Up
orientation. You can initialize an MPImage
with any of the supported UIImage.Orientation
values. Face Landmarker does not support mirrored orientations like .upMirrored
, .downMirrored
, .leftMirrored
, .rightMirrored
.
For more information about UIImage
, refer to the UIImage Apple Developer
Documentation
.
CVPixelBuffer
The CVPixelBuffer
format is well-suited for applications that generate frames
and use the iOS CoreImage
framework for processing.
The CVPixelBuffer
format is well-suited for the following running modes:
-
Images: apps that generate
CVPixelBufferimages after some processing using iOS'sCoreImageframework can be sent to the Face Landmarker in the image running mode. -
Videos: video frames can be converted to the
CVPixelBufferformat for processing, and then sent to the Face Landmarker in video mode. -
livestream: apps using an iOS camera to generate frames may be converted into the
CVPixelBufferformat for processing before being sent to the Face Landmarker in livestream mode.
Swift
// Obtain a CVPixelBuffer. // Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the default // orientation `UIImage.Orientation.up`. let image = try MPImage ( pixelBuffer : pixelBuffer )
Objective-C
// Obtain a CVPixelBuffer. // Convert the `CVPixelBuffer` object to a MediaPipe's Image object having the // default orientation `UIImageOrientationUp`. MPImage * image = [[ MPPImage alloc ] initWithUIImage : image error : nil ];
For more information about CVPixelBuffer
, refer to the CVPixelBuffer Apple
Developer
Documentation
.
CMSampleBuffer
The CMSampleBuffer
format stores media samples of a uniform media type, and is
well-suited for the livestream running mode. Live frames from iOS cameras are
asynchronously delivered in the CMSampleBuffer
format by iOS AVCaptureVideoDataOutput
.
Swift
// Obtain a CMSampleBuffer. // Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the default // orientation `UIImage.Orientation.up`. let image = try MPImage ( sampleBuffer : sampleBuffer )
Objective-C
// Obtain a `CMSampleBuffer`. // Convert the `CMSampleBuffer` object to a MediaPipe's Image object having the // default orientation `UIImageOrientationUp`. MPImage * image = [[ MPPImage alloc ] initWithSampleBuffer : sampleBuffer error : nil ];
For more information about CMSampleBuffer
, refer to the CMSampleBuffer Apple
Developer
Documentation
.
Run the task
To run the Face Landmarker, use the detect()
method specific to the assigned
running mode:
- Still image:
detect(image:) - Video:
detect(videoFrame:timestampInMilliseconds:) - Livestream:
detectAsync(image:timestampInMilliseconds:)
The following code samples show basic examples of how to run Face Landmarker in these different running modes:
Swift
Image
let result = try faceLandmarker.detect(image: image)
Video
let result = try faceLandmarker.detect( videoFrame: image, timestampInMilliseconds: timestamp)
Live stream
try faceLandmarker.detectAsync( image: image, timestampInMilliseconds: timestamp)
Objective-C
Image
MPPFaceLandmarkerResult * result = [ faceLandmarker detectImage : image error : nil ];
Video
MPPFaceLandmarkerResult * result = [ faceLandmarker detectVideoFrame : image timestampInMilliseconds : timestamp error : nil ];
Livestream
BOOL success = [ faceLandmarker detectAsyncImage : image timestampInMilliseconds : timestamp error : nil ];
The Face Landmarker code example shows the implementations of each of these modes
in more detail detect(image:)
, detect(videoFrame:timestampInMilliseconds:)
,
and detectAsync(image:timestampInMilliseconds:)
. The example code allows the
user to switch between processing modes which may not be required for your use
case.
Note the following:
-
When running in video mode or livestream mode, you must also provide the timestamp of the input frame to the Face Landmarker task.
-
When running in image or video mode, the Face Landmarker task blocks the current thread until it finishes processing the input image or frame. To avoid blocking the current thread, execute the processing in a background thread using iOS Dispatch or NSOperation frameworks. If your app is created using Swift, you can also use Swift Concurrency for background thread execution.
-
When running in livestream mode, the Face Landmarker task returns immediately and doesn't block the current thread. It invokes the
faceLandmarker(_:didFinishDetection:timestampInMilliseconds:error:)method with the face landmark detection result after processing each input frame. The Face Landmarker invokes this method asynchronously on a dedicated serial dispatch queue. For displaying results on the user interface, dispatch the results to the main queue after processing the results.
Handle and display results
Upon running inference, the Face Landmarker returns a FaceLandmarkerResult
which
contains a face mesh for each detected face, with coordinates for each face
landmark. Optionally, the result object can also contain blendshapes, which
denote facial expressions, and facial transformation matrices to apply face
effects on the detected landmarks.
The following shows an example of the output data from this task:
FaceLandmarkerResult:
face_landmarks:
NormalizedLandmark #0:
x: 0.5971359014511108
y: 0.485361784696579
z: -0.038440968841314316
NormalizedLandmark #1:
x: 0.3302789330482483
y: 0.29289937019348145
z: -0.09489090740680695 ...
(478 landmarks for each face)
face_blendshapes:
browDownLeft: 0.8296722769737244
browDownRight: 0.8096957206726074
browInnerUp: 0.00035583582939580083
browOuterUpLeft: 0.00035752105759456754 ...
(52 blendshapes for each face)
facial_transformation_matrixes:
[9.99158978e-01, -1.23036895e-02, 3.91213447e-02, -3.70770246e-01]
[1.66496094e-02, 9.93480563e-01, -1.12779640e-01, 2.27719707e+01]
...
The following image shows a visualization of the task output:
The Face Landmarker example code demonstrates how to display the results returned by the task, see FaceOverlay.swift for more details.
