feat(worker): 接口1 四庭七眼测量真实实现（替换 Mock）

worker 侧从 Mock 替换为真实算法：
- face_analysis 包：detector(MediaPipe 478点) / pose(solvePnP 姿态) /
  calibration(虹膜直径法) / hair_segmenter+bisenet_model(方案B 头发分割) /
  measure(方案A兜底+B/A决策+七眼+换算) / annotation(numpy渐变线+中文标注)
- app.py：/api/v1/face/measure 接真实实现，返回 annotated_image_base64
  （不落盘不拼URL，落盘由网关做）；加 X-Internal-Token 鉴权、/health 就绪态、
  可配置分辨率门槛、异常兜底
- 部署：start.sh/run_worker.sh/hair-worker.service 监听 8187；worker_config 示例
- 测试 tests/：Tier1合成真值<1e-6 + Tier2缩放不变 + Tier3叠加 + 错误码集成 +
  数值回归，pytest 24 项全绿
- 文档补实测基线表 + RTX5090/torch 说明

注：worker 为 RTX 5090(sm_120)，pinned torch 2.2.2(cu121) 只到 sm_90，
BiSeNet 已自动回退 CPU（方案B 正常）；要用 GPU 需换 torch cu128(≥2.7)。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/conftest.py

@@ -0,0 +1,74 @@
+"""pytest 公共夹具与合成关键点工具。"""
+import os
+
+import numpy as np
+import pytest
+
+FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures")
+OUTPUT = os.path.join(os.path.dirname(__file__), "output")
+os.makedirs(OUTPUT, exist_ok=True)
+
+
+def fixture(name):
+    return os.path.join(FIXTURES, name)
+
+
+class _LM:
+    """模拟 MediaPipe landmark.x/.y/.z（归一化坐标）。"""
+    def __init__(self, x, y, z=0.0):
+        self.x, self.y, self.z = x, y, z
+
+
+def build_synthetic_landmarks(px_per_cm=50.0, W=1000, H=1000):
+    """按已知 cm 几何摆放关键点，返回 (landmarks_list, ground_truth_dict)。
+
+    坐标与 px_per_cm 都由测试设定，故每段 cm/占比真值已知，测量数学应分毫不差。
+    """
+    cx = W / 2
+
+    def Y(cm_from_top):
+        return (cm_from_top * px_per_cm) / H
+
+    def X(px):
+        return px / W
+
+    gt = {
+        "top_court_cm": 4.0, "upper_court_cm": 5.0,
+        "middle_court_cm": 6.0, "lower_court_cm": 5.0,
+        "eye_width_cm": 3.0, "inter_eye_cm": 3.4, "face_width_cm": 14.0,
+        "px_per_cm": px_per_cm,
+    }
+    y_hairtop = 2.0
+    y_hairline = y_hairtop + gt["top_court_cm"]
+    y_brow = y_hairline + gt["upper_court_cm"]
+    y_nose = y_brow + gt["middle_court_cm"]
+    y_chin = y_nose + gt["lower_court_cm"]
+
+    lm = {i: _LM(X(cx), 0.0) for i in range(478)}
+    # 纵向中轴点
+    lm[9] = _LM(X(cx), Y(y_brow));  lm[151] = _LM(X(cx), Y(y_brow))
+    lm[94] = _LM(X(cx), Y(y_nose))
+    lm[152] = _LM(X(cx), Y(y_chin))
+    # 七眼横向点
+    ew = gt["eye_width_cm"] * px_per_cm
+    ie = gt["inter_eye_cm"] * px_per_cm
+    fw = gt["face_width_cm"] * px_per_cm
+    eye_y = Y(y_brow + 2.0)
+    lm[133] = _LM(X(cx - ie / 2), eye_y);      lm[33] = _LM(X(cx - ie / 2 - ew), eye_y)
+    lm[362] = _LM(X(cx + ie / 2), eye_y);      lm[263] = _LM(X(cx + ie / 2 + ew), eye_y)
+    lm[234] = _LM(X(cx - fw / 2), eye_y);      lm[454] = _LM(X(cx + fw / 2), eye_y)
+    # 虹膜边缘点：直径 = 1.17cm * px_per_cm，使尺度可被精确反解
+    d = 1.17 * px_per_cm
+    lm[469] = _LM(X(cx - ie / 2 - ew / 2 - d / 2), eye_y)
+    lm[471] = _LM(X(cx - ie / 2 - ew / 2 + d / 2), eye_y)
+    lm[474] = _LM(X(cx + ie / 2 + ew / 2 - d / 2), eye_y)
+    lm[476] = _LM(X(cx + ie / 2 + ew / 2 + d / 2), eye_y)
+    return [lm[i] for i in range(478)], gt
+
+
+@pytest.fixture
+def oversize_file(tmp_path):
+    """1006 用例：>1MB 的占位文件（无需合法图片，只看字节数）。"""
+    p = tmp_path / "oversize.bin"
+    p.write_bytes(b"\x00" * 1_100_000)
+    return p