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import numpy as np
import cv2
import mediapipe as mp
LEFT_EYE = [33, 133]
RIGHT_EYE = [362, 263]
NOSE = 1
mp_face_mesh = mp.solutions.face_mesh
def get_lip_engagement(landmarks):
TOP_LIP = 13
BOTTOM_LIP = 14
LIP_LEFT = 78
LIP_RIGHT = 308
top_lip = landmarks[TOP_LIP]
bottom_lip = landmarks[BOTTOM_LIP]
left_corner = landmarks[LIP_LEFT]
right_corner = landmarks[LIP_RIGHT]
lip_opening = abs(top_lip[1] - bottom_lip[1])
lip_width = abs(right_corner[0] - left_corner[0])
# print(f"[DEBUG] lip_opening: {lip_opening:.3f}, lip_width: {lip_width:.3f}")
# Example, adjust as per your actual values!
# This logic: high opening OR high width = Engaged (smile/mouth open)
# very small both = Not Engaged, everything else = Partially Engaged
if lip_opening > 0.01 or lip_width > 0.18:
return "Engaged"
elif lip_opening < 0.002 or lip_width < 0.04:
return "Not Engaged"
else:
return "Partially Engaged"
def track_microexpressions(frame, face_mesh, calibration_ref=None):
if calibration_ref is None:
calibration_ref = {}
h, w, _ = frame.shape
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = face_mesh.process(frame_rgb)
micro = {
"eye_away": False,
"head_turn": False,
}
face_bbox = None
multiple_faces = False
if results.multi_face_landmarks:
if len(results.multi_face_landmarks) > 1:
multiple_faces = True
lm = results.multi_face_landmarks[0].landmark
xs = [p.x for p in lm]
ys = [p.y for p in lm]
xmin, xmax = min(xs)*w, max(xs)*w
ymin, ymax = min(ys)*h, max(ys)*h
face_bbox = [int(xmin), int(ymin), int(xmax), int(ymax)]
eye_x = (lm[LEFT_EYE[0]].x + lm[RIGHT_EYE[0]].x) / 2
nose_x = lm[NOSE].x
margin = 0.07
eye_left_th = calibration_ref.get('eye_left', 0.30)
eye_right_th = calibration_ref.get('eye_right', 0.70)
if eye_x < (eye_left_th - margin) or eye_x > (eye_right_th + margin):
micro["eye_away"] = True
if nose_x < (eye_left_th - margin) or nose_x > (eye_right_th + margin):
micro["head_turn"] = True
return micro, face_bbox, multiple_faces
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