feat(vision): 实现 mingxi-vision 推理服务核心功能

- engine/defect_classes: HRIPCB 6类缺陷定义 + 4类扩展 + severity排序
- engine/base: DefectBox / DetectResult 数据类 + BaseInferenceEngine 抽象基类
- engine/ultralytics_adapter: YOLOv8 .pt 推理适配器 + warmup
- engine/annotator: 按 severity 着色的标注图生成 + base64 编码
- engine/loader: 单例管理 + asyncio.Lock 推理串行化 + 热重载支持
- config: Pydantic v1 BaseSettings，读取 .env
- schema: DetectResponse / HealthResponse / ReloadRequest 等响应模型
- api/detect: POST /api/detect，内存读图，推理锁保护
- api/health: GET /api/health
- api/model: POST /api/model/reload 热重载接口
- main: FastAPI lifespan 启动加载模型

测试通过：/api/health 200，/api/detect 空图返回 pass，标注图 base64 正常

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

mingxi-vision/engine/annotator.py

@@ -0,0 +1,37 @@
+import base64
+from typing import Tuple
+
+import cv2
+import numpy as np
+
+from .base import DetectResult
+
+# 按严重程度着色，BGR格式
+SEVERITY_COLORS: dict = {
+    "fatal":  (0,   0,   220),
+    "major":  (0,   128, 255),
+    "minor":  (0,   215, 255),
+    "rework": (255, 165,   0),
+    "none":   (180, 180, 180),
+}
+
+
+def annotate(image: np.ndarray, result: DetectResult) -> np.ndarray:
+    img = image.copy()
+    for d in result.defects:
+        color: Tuple = SEVERITY_COLORS.get(d.severity, (180, 180, 180))
+        x1, y1, x2, y2 = [int(v) for v in d.box_xyxy]
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+        label = f"{d.class_name_zh} {d.confidence:.2f}"
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        cv2.rectangle(img, (x1, y1 - th - 6), (x1 + tw + 4, y1), color, -1)
+        cv2.putText(img, label, (x1 + 2, y1 - 4),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+    return img
+
+
+def to_base64(image: np.ndarray, quality: int = 85) -> str:
+    ok, buf = cv2.imencode(".jpg", image, [cv2.IMWRITE_JPEG_QUALITY, quality])
+    if not ok:
+        raise RuntimeError("图像编码失败")
+    return base64.b64encode(buf.tobytes()).decode()