ilaandClaude Sonnet 4.6
68e4f444c5
- 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>
38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
import base64
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from typing import Tuple
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import cv2
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import numpy as np
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from .base import DetectResult
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# 按严重程度着色，BGR格式
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SEVERITY_COLORS: dict = {
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"fatal": (0, 0, 220),
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"major": (0, 128, 255),
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"minor": (0, 215, 255),
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"rework": (255, 165, 0),
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"none": (180, 180, 180),
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}
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def annotate(image: np.ndarray, result: DetectResult) -> np.ndarray:
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img = image.copy()
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for d in result.defects:
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color: Tuple = SEVERITY_COLORS.get(d.severity, (180, 180, 180))
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x1, y1, x2, y2 = [int(v) for v in d.box_xyxy]
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
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label = f"{d.class_name_zh} {d.confidence:.2f}"
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(tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv2.rectangle(img, (x1, y1 - th - 6), (x1 + tw + 4, y1), color, -1)
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cv2.putText(img, label, (x1 + 2, y1 - 4),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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return img
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def to_base64(image: np.ndarray, quality: int = 85) -> str:
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ok, buf = cv2.imencode(".jpg", image, [cv2.IMWRITE_JPEG_QUALITY, quality])
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if not ok:
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raise RuntimeError("图像编码失败")
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return base64.b64encode(buf.tobytes()).decode()
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