129 lines
3.6 KiB
Python
129 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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人脸特征提取微服务
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仅提供特征提取 API，供 Java 后端调用
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"""
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import uvicorn
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from fastapi import FastAPI, File, UploadFile, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import List, Optional
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import numpy as np
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import cv2
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import logging
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from face_feature_extractor import FaceFeatureExtractor
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger("FeatureServer")
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# 创建 FastAPI 应用
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app = FastAPI(
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title="Face Feature Extraction Microservice",
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description="Dedicated service for extracting face features",
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version="1.0.0"
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)
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# 允许跨域
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 全局特征提取器实例
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extractor = None
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def get_extractor():
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"""懒加载特征提取器"""
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global extractor
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if extractor is None:
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logger.info("Initializing FaceFeatureExtractor...")
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extractor = FaceFeatureExtractor()
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logger.info("FaceFeatureExtractor initialized.")
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return extractor
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# 响应模型
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class ExtractFeatureResponse(BaseModel):
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success: bool
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message: str
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feature: Optional[List[float]] = None
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feature_dim: Optional[int] = None
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processing_time: Optional[float] = None
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def decode_image(file: UploadFile) -> Optional[np.ndarray]:
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"""解码上传的图片"""
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try:
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contents = file.file.read()
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nparr = np.frombuffer(contents, np.uint8)
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image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return image
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except Exception as e:
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logger.error(f"Image decode failed: {e}")
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return None
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@app.on_event("startup")
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async def startup_event():
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"""启动时预加载模型"""
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get_extractor()
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@app.get("/health")
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async def health_check():
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return {"status": "healthy", "service": "Face Feature Extractor"}
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@app.post("/api/extract_feature", response_model=ExtractFeatureResponse)
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async def extract_feature(image: UploadFile = File(...)):
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"""
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特征提取接口
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输入: 图片文件
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输出: 1024维特征向量
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"""
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try:
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img = decode_image(image)
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if img is None:
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raise HTTPException(status_code=400, detail="Invalid image file")
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ext = get_extractor()
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# 调用提取器
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result = ext.extract_features(img, return_all_faces=False, quality_filter=True)
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if not result.success or not result.faces:
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return ExtractFeatureResponse(
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success=False,
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message=result.error_message or "No face detected",
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processing_time=result.processing_time
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)
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# 获取特征
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feature_vector = result.faces[0].feature.tolist()
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return ExtractFeatureResponse(
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success=True,
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message="Success",
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feature=feature_vector,
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feature_dim=len(feature_vector),
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processing_time=result.processing_time
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)
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except Exception as e:
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logger.error(f"Extraction failed: {e}", exc_info=True)
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return ExtractFeatureResponse(
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success=False,
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message=f"Server error: {str(e)}"
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)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Face Feature Extraction Microservice')
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parser.add_argument('--port', type=int, default=8000, help='Service port')
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args = parser.parse_args()
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logger.info(f"Starting Feature Server on port {args.port}...")
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uvicorn.run(app, host="0.0.0.0", port=args.port)
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