Paste it to ScriptCHOP

TouchDesigner-FaceDetection-Google_MediaPipe.py

# me - this DAT
# scriptOp - the OP which is cooking
import mediapipe as mp
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

"""
https://google.github.io/mediapipe/solutions/face_detection.html

MODEL_SELECTION
An integer index 0 or 1. Use 0 to select a short-range model that works best for faces within 2 meters from the camera, and 1 for a full-range model best for faces within 5 meters. For the full-range option, a sparse model is used for its improved inference speed. Please refer to the model cards for details. Default to 0 if not specified.

MIN_DETECTION_CONFIDENCE
Minimum confidence value ([0.0, 1.0]) from the face detection model for the detection to be considered successful. Default to 0.5.
"""
face_detection = mp_face_detection.FaceDetection(
	model_selection=0,
	min_detection_confidence=0.5)

NUM_FACES_SLOTS = 2

def onCook(scriptOp):
	scriptOp.clear()

	nA = op('src').numpyArray()
	nA = nA[:,:,:3] * 255 # rgba float --> rgb uint
	nA = nA.astype('uint8')
	results = face_detection.process(nA)

	"""
	The channel name and coordinate should be same to FaceTrackCHOP 
	https://docs.derivative.ca/Face_Track_CHOP
	"""
	for i in range(0, NUM_FACES_SLOTS):
		index = i + 1
		valid = scriptOp.appendChan('face{}:valid'.format(index))
		width = scriptOp.appendChan('face{}/bbox:width'.format(index))
		height = scriptOp.appendChan('face{}/bbox:height'.format(index))
		u = scriptOp.appendChan('face{}/bbox:u'.format(index))
		v = scriptOp.appendChan('face{}/bbox:v'.format(index))

		if results.detections and i < len(results.detections):
			detection = results.detections[i]
			valid[0] = 1 # detection.score
			width[0] = detection.location_data.relative_bounding_box.width
			height[0] = detection.location_data.relative_bounding_box.height
			u[0] = detection.location_data.relative_bounding_box.xmin + width[0]/2
			v[0] = detection.location_data.relative_bounding_box.ymin + height[0]/2
	return