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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)))} | |