console.log("Hello, World!");Face landmark detection guide for Python
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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 and apply facial filters and effects to create a virtual avatar. This task uses machine learning (ML) models that can work with single images 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. For more information about the capabilities, models, and configuration options of this task, see the Overview.
Code example
The example code for Face Landmarker provides a complete implementation of this task in Python for your reference. This code helps you test this task and get started on building your own face landmarker. You can view, run, and edit the Face Landmarker example code using just your web browser.
Setup
This section describes key steps for setting up your development environment and code projects specifically 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 Python.
Attention: This MediaPipe Solutions Preview is an early release. Learn more.
Packages
The MediaPipe Face Landmarker task requires the mediapipe PyPI package. You can install and import these dependencies with the following:
$ python -m pip install mediapipe
Imports
Import the following classes to access the Face Landmarker task functions:
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
Model
The MediaPipe Face Landmarker task requires a trained model that is compatible with this task. For more information on available trained models for Face Landmarker, see the task overview Models section.
Select and download the model, and then store it in a local directory:
model_path = '/absolute/path/to/face_landmarker.task'
Use the BaseOptions object model_asset_path parameter to specify the path of the model to use. For a code example, see the next section.
Create the task
The MediaPipe Face Landmarker task uses the create_from_options function to set up the task. The create_from_options function accepts values for configuration options to handle. For more information on configuration options, see Configuration options.
The following code demonstrates how to build and configure this task.
These samples also show the variations of the task construction for images, video files, and live stream.
Image
Video
Live stream
import mediapipe as mp
BaseOptions = mp.tasks.BaseOptions
FaceLandmarker = mp.tasks.vision.FaceLandmarker
FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
VisionRunningMode = mp.tasks.vision.RunningMode
options = FaceLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=VisionRunningMode.IMAGE)
with FaceLandmarker.create_from_options(options) as landmarker:
# The landmarker is initialized. Use it here.
# ...
Note: If you use the video mode or live stream mode, Face Landmarker uses tracking to avoid triggering the model on every frame, which helps reduce latency.
For a complete example of creating a Face Landmarker for use with an image, see the code example.
Configuration options
This task has the following configuration options for Python applications:
Option Name Description Value Range Default Value
running_mode Sets the running mode for the task. The landmarker has the following modes:
IMAGE: The mode for recognizing face landmarks on single image inputs.
VIDEO: The mode for recognizing face landmarks on the decoded frames of a video.
LIVE_STREAM: The mode for recognizing face landmarks on a live stream of input data, such as from camera. In this mode, result_callback must be called to set up a listener to receive the recognition results asynchronously.
{IMAGE, VIDEO, LIVE_STREAM} IMAGE
num_faces The maximum number of faces that can be detected by the the FaceLandmarker. Smoothing is only applied when num_faces is set to 1. Integer > 0 1
min_face_detection_confidence The minimum confidence score for the face detection to be considered successful. Float [0.0,1.0] 0.5
min_face_presence_confidence The minimum confidence score of face presence score in the face landmark detection. Float [0.0,1.0] 0.5
min_tracking_confidence The minimum confidence score for the face tracking to be considered successful. Float [0.0,1.0] 0.5
output_face_blendshapes Whether Face Landmarker outputs face blendshapes. Face blendshapes are used for rendering the 3D face model. Boolean False
output_facial_transformation_matrixes 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. Boolean False
result_callback Sets the result listener to receive the landmarker results asynchronously when FaceLandmarker is in the live stream mode. Can only be used when running mode is set to LIVE_STREAM ResultListener N/A
Prepare data
Prepare your input as an image file or a numpy array, then convert it to a mediapipe.Image object. If your input is a video file or live stream from a webcam, you can use an external library such as OpenCV to load your input frames as numpy arrays.
Image
Video
Live stream
import mediapipe as mp
# Load the input image from an image file.
mp_image = mp.Image.create_from_file('/path/to/image')
# Load the input image from a numpy array.
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=numpy_image)
Run the task
The Face Landmarker uses the detect, detect_for_video and detect_async functions to trigger inferences. For face landmarking, this involves preprocessing input data and detecting faces in the image.
The following code demonstrates how to execute the processing with the task model.
Image
Video
Live stream
# Perform face landmarking on the provided single image.
# The face landmarker must be created with the image mode.
face_landmarker_result = landmarker.detect(mp_image)
Note the following:
When running in the video mode or the live stream mode, also provide the Face Landmarker task the timestamp of the input frame.
When running in the image or the video model, the Face Landmarker task blocks the current thread until it finishes processing the input image or frame.
When running in the live stream mode, the Face Landmarker task returns immediately and doesn’t block the current thread. It will invoke the result listener with the detection result every time it finishes processing an input frame. If the detection function is called when the Face Landmarker task is busy processing another frame, the task will ignore the new input frame.
For a complete example of running an Face Landmarker on an image, see the code example for details.
Handle and display results
The Face Landmarker returns a FaceLandmarkerResult object for each detection run. The result object 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 a facial transformation matrix 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 from the task, see the code example for details.
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