feat: 新增52号发际线接口并限制GPU worker来源IP

保留5号历史三档实现，新增中下庭高度定位、扶正裁切、矩形叠加与30案例对比页。

worker仅允许本机和配置白名单访问；网关不增加IP限制。空图片URL字段改写限定为52号，保持旧接口兼容。同步接口、裁切与部署文档。

验证：113项测试通过；1项历史数值基线失败，在未修改的a3fddbb版本复现。浏览器验证5/52及30案例对比页通过。

hairline/height_rule.py

@@ -0,0 +1,117 @@
+"""接口52设计高度：上庭 = (中庭 + 下庭) / 2；不改变接口2/5的几何。"""
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from .mask import mask_from_curve
+
+
+def height_target(landmarks, width, height, reference=None):
+    """Use unrounded measurement coordinates; fall back to the same MP indices.
+
+    Distances use image Y, matching face_analysis.measure's court heights.
+    No rounded centimetre values or existing-hairline estimates enter the rule.
+    """
+    scale = np.array([width, height], dtype=np.float64)
+    if reference is None:
+        brow, nose, chin = landmarks[[9, 94, 152], :2] * scale
+        source = "hairline_landmarks"
+        px_per_cm = None
+    else:
+        brow = np.asarray(reference["brow_center"], dtype=np.float64)
+        nose = np.asarray(reference["nose_bottom"], dtype=np.float64)
+        chin = np.asarray(reference["chin_tip"], dtype=np.float64)
+        source = "face_measure"
+        px_per_cm = reference.get("px_per_cm")
+    if not np.isfinite([brow, nose, chin]).all() or not brow[1] < nose[1] < chin[1]:
+        raise ValueError("眉心、鼻翼下缘与下巴坐标异常，无法计算目标上庭高度")
+    middle, lower = float(nose[1] - brow[1]), float(chin[1] - nose[1])
+    upper = (middle + lower) / 2.0
+    target_y = float(brow[1] - upper)
+    if not (0 <= brow[0] < width and 1 <= target_y < height - 1):
+        raise ValueError("按中下庭均值计算的目标发际线超出图片范围，请使用完整正面照")
+    def pt(p):
+        return {"x": float(p[0]), "y": float(p[1])}
+    return {
+        "name": "court_average",
+        "formula": "上庭 = (中庭 + 下庭) / 2",
+        "measurement_source": source,
+        "brow_center": pt(brow), "nose_bottom": pt(nose), "chin_tip": pt(chin),
+        "middle_height_px": middle, "lower_height_px": lower,
+        "target_upper_height_px": upper,
+        "target_upper_height_cm": upper / px_per_cm if px_per_cm else None,
+        "target_center": {"x": float(brow[0]), "y": target_y},
+    }
+
+
+def clean_curve(overlay):
+    """Keep the white design line; discard opaque black texture-border pixels."""
+    out = np.zeros_like(overlay, dtype=np.float32)
+    out[:, :, :3] = 255
+    out[:, :, 3] = overlay[:, :, 3] * (overlay[:, :, :3].mean(axis=2) > 128)
+    return out
+
+
+def curve_center(overlay, center_x):
+    """Alpha-weighted centre of the line at the facial midline, in image pixels."""
+    h, w = overlay.shape[:2]
+    column = int(round(center_x))
+    if not 0 <= column < w:
+        raise ValueError("面部中轴超出图片范围")
+    alpha = overlay[:, column, 3].astype(np.float64)
+    alpha[alpha <= 1] = 0
+    if not alpha.any():
+        # Rasterized endpoints may leave a 1px gap, but distant sideburns
+        # must never be silently substituted for a missing central curve.
+        columns = np.flatnonzero((overlay[:, :, 3] > 1).any(axis=0))
+        if not columns.size or np.min(np.abs(columns - column)) > 2:
+            raise ValueError("发际线模板在面部中轴处没有有效曲线")
+        column = int(columns[np.argmin(np.abs(columns - column))])
+        alpha = overlay[:, column, 3].astype(np.float64)
+        alpha[alpha <= 1] = 0
+    return {"x": float(center_x), "y": float(np.dot(np.arange(h), alpha) / alpha.sum())}
+
+
+def place_curve(overlay, target, landmarks):
+    """Rigidly shift a rendered template along face-up to meet the target Y.
+
+    Keeping the curve rigid preserves roll and the template's shape. Pixel Y
+    remains the height convention, even for a rolled face. A short correction
+    compensates for the curve's slope as the local-axis shift also changes X.
+    """
+    curve = clean_curve(overlay)
+    h, w = curve.shape[:2]
+    center_x, target_y = target["x"], target["y"]
+    before = curve_center(curve, center_x)
+    up = (landmarks[10, :2] - landmarks[152, :2]) * [w, h]
+    if abs(up[1]) < 1e-6:
+        raise ValueError("无法确定人脸向上方向")
+    x_per_y = float(np.clip(up[0] / up[1], -1.0, 1.0))
+    dy = target_y - before["y"]
+    moved = curve
+    for _ in range(8):
+        transform = np.array([[1, 0, dy * x_per_y], [0, 1, dy]], np.float32)
+        moved = cv2.warpAffine(curve, transform, (w, h), flags=cv2.INTER_LINEAR,
+                               borderMode=cv2.BORDER_CONSTANT, borderValue=0)
+        center = curve_center(moved, center_x)
+        residual = target_y - center["y"]
+        if abs(residual) <= 0.15:
+            break
+        dy += residual
+    if abs(center["y"] - target_y) > 1.0:
+        raise ValueError("发际线中心无法对齐目标高度，请检查照片姿态与裁剪范围")
+    return moved, {
+        "old_center": before, "new_center": center,
+        "shift_x_px": float(dy * x_per_y), "shift_y_px": float(dy),
+        "target_error_px": float(abs(center["y"] - target_y)),
+    }
+
+
+def inpaint_from_curve(photo, landmarks, parse_map, overlay):
+    """The preview line is also the exact boundary used for the black line/mask."""
+    alpha = np.clip(overlay[:, :, 3:4] / 255.0, 0, 1)
+    marked = np.clip(photo * (1.0 - alpha), 0, 255).astype(np.uint8)
+    curve = (overlay[:, :, 3] > 40).astype(np.uint8) * 255
+    mask = mask_from_curve(curve, landmarks, parse_map)
+    return marked, mask