Face Tracking

Python

Use the MediaPipe Face Detector and Landmarker solutions to detect and track a human face in image, videos, and camera stream.

pip install -r examples/python/face_tracking/requirements.txt
python examples/python/face_tracking/main.py

Usage

CLI usage help is available using the --help option:

$ python examples/python/face_tracking/main.py --help
usage: main.py [-h] [--demo-image] [--image IMAGE] [--video VIDEO] [--camera CAMERA] [--max-frame MAX_FRAME] [--max-dim MAX_DIM] [--num-faces NUM_FACES] [--headless] [--connect] [--serve] [--addr ADDR] [--save SAVE]

Uses the MediaPipe Face Detection to track a human pose in video.

options:
  -h, --help            show this help message and exit
  --demo-image          Run on a demo image automatically downloaded
  --image IMAGE         Run on the provided image
  --video VIDEO         Run on the provided video file.
  --camera CAMERA       Run from the camera stream (parameter is the camera ID, usually 0
  --max-frame MAX_FRAME
                        Stop after processing this many frames. If not specified, will run until interrupted.
  --max-dim MAX_DIM     Resize the image such as its maximum dimension is not larger than this value.
  --num-faces NUM_FACES
                        Max number of faces detected by the landmark model (temporal smoothing is applied only for a value of 1).
  --headless            Don't show GUI
  --connect             Connect to an external viewer
  --serve               Serve a web viewer (WARNING: experimental feature)
  --addr ADDR           Connect to this ip:port
  --save SAVE           Save data to a .rrd file at this path

Here is an overview of the options specific to this example:

• Running modes: By default, this example streams images from the default webcam. Another webcam can be used by providing a camera index with the --camera option. Alternatively, images can be read from a video file (using --video PATH) or a single image file (using --image PATH). Also, a demo image with two faces can be automatically downloaded and used with --demo-image.
• Max face count: The maximum face detected by MediaPipe Face Landmarker can be set using --num-faces NUM. It defaults to 1, in which case the Landmarker applies temporal smoothing. This parameter doesn't affect MediaPipe Face Detector, which always attempts to detect all faces present in the input images.
• Image downscaling: By default, this example logs and runs on the native resolution of the provided images. Input images can be downscaled to a given maximum dimension using --max-dim DIM.
• Limiting frame count: When running from a webcam or a video file, this example can be set to stop after a given number of frames using --max-frame MAX_FRAME.