Face landmark detection guide for Python

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.

If you are implementing the Face Landmarker for Raspberry Pi, refer to the Raspberry Pi example app.

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.

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.

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.
  # ...
    

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
FaceLandmarker = mp.tasks.vision.FaceLandmarker
FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
VisionRunningMode = mp.tasks.vision.RunningMode

# Create a face landmarker instance with the video mode:
options = FaceLandmarkerOptions(
    base_options=BaseOptions(model_asset_path=model_path),
    running_mode=VisionRunningMode.VIDEO)

with FaceLandmarker.create_from_options(options) as landmarker:
  # The landmarker is initialized. Use it here.
  # ...
    

import mediapipe as mp

BaseOptions = mp.tasks.BaseOptions
FaceLandmarker = mp.tasks.vision.FaceLandmarker
FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
FaceLandmarkerResult = mp.tasks.vision.FaceLandmarkerResult
VisionRunningMode = mp.tasks.vision.RunningMode

# Create a face landmarker instance with the live stream mode:
def print_result(result: FaceLandmarkerResult, output_image: mp.Image, timestamp_ms: int):
    print('face landmarker result: {}'.format(result))

options = FaceLandmarkerOptions(
    base_options=BaseOptions(model_asset_path=model_path),
    running_mode=VisionRunningMode.LIVE_STREAM,
    result_callback=print_result)

with FaceLandmarker.create_from_options(options) as landmarker:
  # The landmarker is initialized. Use it here.
  # ...
    

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:

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.

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)
    

import mediapipe as mp

# Use OpenCV’s VideoCapture to load the input video.

# Load the frame rate of the video using OpenCV’s CV_CAP_PROP_FPS
# You’ll need it to calculate the timestamp for each frame.

# Loop through each frame in the video using VideoCapture#read()

# Convert the frame received from OpenCV to a MediaPipe’s Image object.
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=numpy_frame_from_opencv)
    

import mediapipe as mp

# Use OpenCV’s VideoCapture to start capturing from the webcam.

# Create a loop to read the latest frame from the camera using VideoCapture#read()

# Convert the frame received from OpenCV to a MediaPipe’s Image object.
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=numpy_frame_from_opencv)
    

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.

# 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)
    

# Perform face landmarking on the provided single image.
# The face landmarker must be created with the video mode.
face_landmarker_result = landmarker.detect_for_video(mp_image, frame_timestamp_ms)
    

# Send live image data to perform face landmarking.
# The results are accessible via the `result_callback` provided in
# the `FaceLandmarkerOptions` object.
# The face landmarker must be created with the live stream mode.
landmarker.detect_async(mp_image, frame_timestamp_ms)
    

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.