Delete segmentation.py

segmentation.py

@@ -1,69 +0,0 @@
-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 a binary mask (uint8 0/255) for hair/head region using MediaPipe if available,
-    otherwise falls back to naive ellipse.
-    """
-    if not _HAS_MP:
-        # Fallback to naive ellipse
-        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
-
-    # Use MediaPipe FaceMesh for better head segmentation
-    mp_face = mp.solutions.face_mesh
-    with mp_face.FaceMesh(static_image_mode=True, max_num_faces=1) as face_mesh:
-        results = face_mesh.process(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB))
-        if not results.multi_face_landmarks:
-            return np.zeros(img_np.shape[:2], dtype=np.uint8)  # Empty mask if no face
-
-        landmarks = results.multi_face_landmarks[0]
-        h, w = img_np.shape[:2]
-        mask = np.zeros((h, w), dtype=np.uint8)
-
-        # Define head contour points (approximate hair region using face landmarks)
-        head_points = [
-            10,  # Top forehead
-            109, 67, 103, 54, 21, 162, 127, 234, 93, 132, 215,  # Left side
-            338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,  # Right side
-            152  # Chin
-        ]
-        points = np.array([(int(landmarks.landmark[i].x * w), int(landmarks.landmark[i].y * h)) for i in head_points])
-
-        # Expand slightly for hair
-        hull = cv2.convexHull(points)
-        cv2.fillConvexPoly(mask, hull, 255)
-
-        # Dilate to cover more hair area
-        kernel = np.ones((15, 15), np.uint8)
-        mask = cv2.dilate(mask, kernel, iterations=2)
-
-        return mask
-
-def estimate_landmarks(img_np):
-    """
-    Return dict with key landmarks for alignment (e.g., forehead anchor).
-    """
-    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 face_mesh:
-        results = face_mesh.process(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB))
-        if not results.multi_face_landmarks:
-            return None
-        lm = results.multi_face_landmarks[0].landmark
-        # Forehead anchor (average of top landmarks)
-        xs = [p.x for p in lm[:10]]  # Normalized [0,1]
-        ys = [p.y for p in lm[:10]]
-        h, w = img_np.shape[:2]
-        return {"forehead_anchor": (int(np.mean(xs) * w), int(np.mean(ys) * h))}