Create segmentation.py

segmentation.py

@@ -0,0 +1,30 @@
+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)))}