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import numpy as np | |
import cv2 | |
try: | |
import mediapipe as mp | |
_HAS_MP = True | |
except Exception: | |
_HAS_MP = False | |
def segment_image(img_np): | |
"""Returns binary mask for hair/head.""" | |
h, w = img_np.shape[:2] | |
mask = np.zeros((h, w), dtype=np.uint8) | |
center = (w // 2, int(h * 0.38)) | |
axes = (int(w * 0.28), int(h * 0.33)) | |
cv2.ellipse(mask, center, axes, 0, 0, 360, 255, -1) | |
return mask | |
def estimate_landmarks(img_np): | |
if not _HAS_MP: | |
return None | |
mp_face = mp.solutions.face_mesh | |
with mp_face.FaceMesh(static_image_mode=True, max_num_faces=1) as fm: | |
results = fm.process(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)) | |
if not results.multi_face_landmarks: | |
return None | |
lm = results.multi_face_landmarks[0] | |
xs = [p.x for p in lm.landmark[:10]] | |
ys = [p.y for p in lm.landmark[:10]] | |
return {"forehead_anchor": (float(np.mean(xs)), float(np.mean(ys)))} | |