This example uses MediaPipe with Python on a Raspberry Pi to perform real-time face landmarks detection using images streamed from the camera.

Before you begin, you need to set up your Raspberry Pi with Raspberry 64-bit Pi OS (preferably updated to Buster).

You also need to connect and configure the Pi Camera if you use the Pi Camera. This code also works with USB camera connect to the Raspberry Pi.

And to see the results from the camera, you need a monitor connected to the Raspberry Pi. It's okay if you're using SSH to access the Pi shell (you don't need to use a keyboard connected to the Pi)—you only need a monitor attached to the Pi to see the camera stream.

You can install the required dependencies using the setup.sh script provided with this project.

First, clone this Git repo onto your Raspberry Pi.

Run this script to install the required dependencies and download the task file:

cd mediapipe/examples/face_landmarker/raspberry_pi
sh setup.sh

python3 detect.py

• You can optionally specify the model parameter to set the task file to be used:
  • The default value is face_landmarker.task
  • TensorFlow Lite face landmarker models with metadata
    • Models from MediaPipe Models
• You can optionally specify the numFaces parameter to the maximum number of faces that can be detected by the landmarker:
  • Supported value: A positive integer.
  • Default value: 1
• You can optionally specify the minFaceDetectionConfidence parameter to adjust the minimum confidence score for face detection to be considered successful:
  • Supported value: A floating-point number.
  • Default value: 0.5
• You can optionally specify the minFacePresenceConfidence parameter to adjust the minimum confidence score of hand presence score in the face landmark detection:
  • Supported value: A floating-point number.
  • Default value: 0.5
• You can optionally specify the minTrackingConfidence parameter to adjust the minimum confidence score for the face tracking to be considered successful:
  • Supported value: A floating-point number.
  • Default value: 0.5
• Example usage:

  python3 detect.py \
    --model face_landmarker.task \
    --numFaces 2 \
    --minFaceDetectionConfidence 0.5