{"id":91829,"date":"2026-08-08T13:42:00","date_gmt":"2026-08-08T05:42:00","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/91829.html"},"modified":"2026-08-08T13:42:00","modified_gmt":"2026-08-08T05:42:00","slug":"%e3%80%90%e5%85%8d%e8%b4%b9%e3%80%91%e4%ba%ba%e8%84%b8%e8%af%86%e5%88%ab-%e6%99%ba%e8%83%bd%e9%97%a8%e7%a6%81%e7%b3%bb%e7%bb%9f%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0opencv-dnnfastapivue3-%e9%94%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/91829.html","title":{"rendered":"\u3010\u514d\u8d39\u3011\u4eba\u8138\u8bc6\u522b \u667a\u80fd\u95e8\u7981\u7cfb\u7edf(\u6df1\u5ea6\u5b66\u4e60+OpenCV DNN+FastAPI+Vue3) \u950b\u54e5\u539f\u521b\u51fa\u54c1\uff0c\u5fc5\u5c5e\u7cbe\u54c1"},"content":{"rendered":"

\u5927\u5bb6\u597d，\u6211\u662fJava1234_\u5c0f\u950b\u8001\u5e08，\u5206\u4eab\u4e00\u5957\u950b\u54e5\u539f\u521b\u7684\u4eba\u8138\u8bc6\u522b \u667a\u80fd\u95e8\u7981\u7cfb\u7edf(\u6df1\u5ea6\u5b66\u4e60+OpenCV DNN+FastAPI+Vue3)<\/p>\n

\"\"<\/p>\n

\u9879\u76ee\u4ecb\u7ecd<\/h3>\n

\u51fa\u5165\u63a7\u5236\u662f\u5b89\u9632\u4f53\u7cfb\u4e2d\u7684\u57fa\u7840\u73af\u8282\u3002\u957f\u671f\u4ee5\u6765，IC \u5361\u3001\u5bc6\u7801\u952e\u76d8\u548c\u95e8\u7981\u94a5\u5319\u5728\u4f01\u4e8b\u4e1a\u5355\u4f4d\u4e2d\u5e7f\u6cdb\u4f7f\u7528，\u4f46\u5b58\u5728\u5361\u7247\u6613\u4e22\u5931\u3001\u5bc6\u7801\u6613\u6cc4\u9732\u3001\u5192\u7528\u96be\u8ffd\u6eaf\u7b49\u95ee\u9898\u3002\u5c24\u5176\u5728\u4eba\u5458\u6d41\u52a8\u6027\u8f83\u9ad8\u7684\u56ed\u533a\u4e0e\u5199\u5b57\u697c\u573a\u666f\u4e2d，\u5361\u7247\u8865\u529e\u6210\u672c\u9ad8，\u6743\u9650\u53d8\u66f4\u5f80\u5f80\u6ede\u540e，\u7ba1\u7406\u65b9\u96be\u4ee5\u53ca\u65f6\u638c\u63e1\u201c\u8c01\u5728\u4f55\u65f6\u4ece\u4f55\u5904\u8fdb\u51fa\u201d\u7684\u5b8c\u6574\u4fe1\u606f\u3002\u8fd1\u5e74\u6765，\u6df1\u5ea6\u5b66\u4e60\u63a8\u52a8\u751f\u7269\u7279\u5f81\u8bc6\u522b\u6280\u672f\u5feb\u901f\u843d\u5730，\u4eba\u8138\u8bc6\u522b\u56e0\u91c7\u96c6\u6210\u672c\u4f4e\u3001\u901a\u884c\u4f53\u9a8c\u597d\u3001\u53ef\u4e0e\u89c6\u9891\u76d1\u63a7\u8054\u52a8，\u6210\u4e3a\u667a\u80fd\u95e8\u7981\u7684\u4e3b\u6d41\u65b9\u6848\u4e4b\u4e00\u3002<\/p>\n

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\u672c\u8bfe\u9898\u9009\u62e9 OpenCV DNN\u3001YuNet \u4e0e SFace \u4f5c\u4e3a\u8bc6\u522b\u6280\u672f\u8def\u7ebf，\u7ed3\u5408 Python\u3001FastAPI \u4e0e Vue3 \u5b8c\u6210\u7cfb\u7edf\u5f00\u53d1\u3002\u8be5\u65b9\u6848\u65e0\u9700\u4f9d\u8d56\u5546\u4e1a\u95ed\u6e90 SDK，\u6a21\u578b\u6587\u4ef6\u4f53\u79ef\u8f83\u5c0f，\u90e8\u7f72\u95e8\u69db\u4f4e，\u9002\u5408\u672c\u79d1\u6bd5\u4e1a\u8bbe\u8ba1\u5728\u6709\u9650\u786c\u4ef6\u6761\u4ef6\u4e0b\u5b8c\u6210\u53ef\u8fd0\u884c\u539f\u578b，\u540c\u65f6\u53c8\u80fd\u4f53\u73b0\u6df1\u5ea6\u5b66\u4e60\u5728\u5b9e\u9645\u5de5\u7a0b\u4e2d\u7684\u5e94\u7528\u65b9\u5f0f\u3002\u4e0e\u5355\u7eaf\u8c03\u7528\u4e91\u7aef\u8bc6\u522b API \u76f8\u6bd4，\u672c\u5730\u5316\u63a8\u7406\u66f4\u6709\u5229\u4e8e\u4fdd\u62a4\u4eba\u8138\u751f\u7269\u7279\u5f81\u6570\u636e，\u4e5f\u4fbf\u4e8e\u5728\u8bba\u6587\u4e2d\u5b8c\u6574\u5c55\u793a\u7b97\u6cd5\u8c03\u7528\u94fe\u8def\u4e0e\u7cfb\u7edf\u8bbe\u8ba1\u7ec6\u8282\u3002<\/p>\n

\u4ece\u4eba\u624d\u57f9\u517b\u89d2\u5ea6\u770b，\u672c\u8bfe\u9898\u6a2a\u8de8\u8f6f\u4ef6\u5de5\u7a0b\u3001\u6570\u636e\u5e93\u539f\u7406\u3001Web \u5f00\u53d1\u4e0e\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u591a\u4e2a\u77e5\u8bc6\u6a21\u5757，\u8981\u6c42\u5b66\u751f\u628a\u8bfe\u5802\u7406\u8bba\u8f6c\u5316\u4e3a\u53ef\u8fd0\u884c\u7cfb\u7edf，\u953b\u70bc\u9700\u6c42\u5206\u6790\u3001\u6a21\u5757\u5212\u5206\u3001\u63a5\u53e3\u8bbe\u8ba1\u3001\u6d4b\u8bd5\u9a8c\u8bc1\u548c\u8bba\u6587\u8868\u8fbe\u80fd\u529b，\u7b26\u5408\u672c\u79d1\u6bd5\u4e1a\u8bbe\u8ba1\u201c\u7efc\u5408\u8bad\u7ec3\u201d\u7684\u5b9a\u4f4d\u3002<\/p>\n

\u6e90\u7801\u4e0b\u8f7d<\/h3>\n

\u94fe\u63a5: https:\/\/pan.baidu.com\/s\/1XF5Bj0hyipdAXhTywhfmmg?pwd=1234 \u63d0\u53d6\u7801: 1234<\/p>\n<\/p>\n

\u7cfb\u7edf\u5c55\u793a \"\"\"\"\"\"\"\"\"\"\"\"<\/h3>\n

\"\"\"\"\"\"<\/h3>\n

\u6838\u5fc3\u4ee3\u7801<\/h3>\n

"""
\nOpenCV DNN \u4eba\u8138\u8bc6\u522b\u5f15\u64ce
\n\u4f7f\u7528 YuNet \u4eba\u8138\u68c0\u6d4b + SFace \u7279\u5f81\u63d0\u53d6
\n"""
\nimport os
\nfrom typing import Optional, Tuple<\/p>\n

import cv2
\nimport numpy as np<\/p>\n

from app.core.config import settings<\/p>\n

class FaceEngine:
\n """\u4eba\u8138\u8bc6\u522b\u5f15\u64ce\u5355\u4f8b\u7c7b"""<\/p>\n

_instance = None<\/p>\n

def __new__(cls):
\n if cls._instance is None:
\n cls._instance = super().__new__(cls)
\n cls._instance._initialized = False
\n return cls._instance<\/p>\n

def __init__(self):
\n if self._initialized:
\n return
\n self.detector = None
\n self.recognizer = None
\n self._initialized = True<\/p>\n

def load_models(self):
\n """
\n \u52a0\u8f7d YuNet \u548c SFace \u6a21\u578b
\n """
\n models_dir = settings.MODELS_DIR
\n os.makedirs(models_dir, exist_ok=True)
\n yunet_path = os.path.join(models_dir, "face_detection_yunet_2023mar.onnx")
\n sface_path = os.path.join(models_dir, "face_recognition_sface_2021dec.onnx")<\/p>\n

if not os.path.exists(yunet_path) or not os.path.exists(sface_path):
\n raise FileNotFoundError(f"\u6a21\u578b\u6587\u4ef6\u4e0d\u5b58\u5728，\u8bf7\u5148\u8fd0\u884c scripts\/download_models.py \u4e0b\u8f7d\u6a21\u578b")<\/p>\n

self.detector = cv2.FaceDetectorYN.create(yunet_path, "", (320, 320), 0.6, 0.3, 5000)
\n self.recognizer = cv2.FaceRecognizerSF.create(sface_path, "")
\n print("[FaceEngine] \u6a21\u578b\u52a0\u8f7d\u6210\u529f")<\/p>\n

def detect_largest_face(self, image: np.ndarray) -> Optional[np.ndarray]:
\n """
\n \u68c0\u6d4b\u56fe\u50cf\u4e2d\u6700\u5927\u7684\u4eba\u8138
\n Args:
\n image: BGR\u683c\u5f0f\u56fe\u50cf
\n Returns:
\n \u4eba\u8138\u68c0\u6d4b\u7ed3\u679c\u884c\u5411\u91cf [x,y,w,h,…] \u6216 None
\n """
\n if self.detector is None:
\n self.load_models()
\n h, w = image.shape[:2]
\n self.detector.setInputSize((w, h))
\n _, faces = self.detector.detect(image)
\n if faces is None or len(faces) == 0:
\n return None
\n areas = faces[:, 2] * faces[:, 3]
\n return faces[np.argmax(areas)]<\/p>\n

def extract_feature(self, image: np.ndarray, face: np.ndarray) -> Optional[np.ndarray]:
\n """
\n \u63d0\u53d6\u4eba\u8138128\u7ef4\u7279\u5f81\u5411\u91cf
\n Args:
\n image: BGR\u683c\u5f0f\u56fe\u50cf
\n face: \u4eba\u8138\u68c0\u6d4b\u7ed3\u679c
\n Returns:
\n 128\u7ef4\u7279\u5f81\u5411\u91cf
\n """
\n if self.recognizer is None:
\n self.load_models()
\n aligned = self.recognizer.alignCrop(image, face)
\n feature = self.recognizer.feature(aligned)
\n return feature.flatten()<\/p>\n

def process_image(self, image: np.ndarray) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
\n """
\n \u68c0\u6d4b\u5e76\u63d0\u53d6\u7279\u5f81（\u4e00\u6b65\u5b8c\u6210）
\n Args:
\n image: BGR\u683c\u5f0f\u56fe\u50cf
\n Returns:
\n (\u7279\u5f81\u5411\u91cf, \u4eba\u8138\u6846) \u6216 (None, None)
\n """
\n face = self.detect_largest_face(image)
\n if face is None:
\n return None, None
\n feature = self.extract_feature(image, face)
\n return feature, face<\/p>\n

@staticmethod
\n def cosine_similarity(feat1: np.ndarray, feat2: np.ndarray) -> float:
\n """
\n \u8ba1\u7b97\u4e24\u4e2a\u7279\u5f81\u5411\u91cf\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6
\n """
\n norm1 = np.linalg.norm(feat1)
\n norm2 = np.linalg.norm(feat2)
\n if norm1 == 0 or norm2 == 0:
\n return 0.0
\n return float(np.dot(feat1, feat2) \/ (norm1 * norm2))<\/p>\n

@staticmethod
\n def decode_image(image_bytes: bytes) -> np.ndarray:
\n """\u5c06\u5b57\u8282\u89e3\u7801\u4e3aOpenCV\u56fe\u50cf"""
\n nparr = np.frombuffer(image_bytes, np.uint8)
\n return cv2.imdecode(nparr, cv2.IMREAD_COLOR)<\/p>\n

face_engine = FaceEngine()<\/p>\n

<template>
\n <div class="page-container">
\n <div class="page-header"><h2>\u6211\u7684\u4eba\u8138<\/h2><\/div>
\n <div class="face-grid">
\n <div v-for="f in faceList" :key="f.id" class="face-card">
\n <el-image :src="f.image_url" fit="cover" style="width:160px;height:160px;border-radius:12px" \/>
\n <div class="face-info">
\n <el-tag v-if="f.is_primary" type="success" size="small">\u4e3b\u56fe<\/el-tag>
\n <span v-if="f.quality_score">\u8d28\u91cf: {{ f.quality_score }}<\/span>
\n <span>{{ f.create_time }}<\/span>
\n <\/div>
\n <\/div>
\n <el-empty v-if="!faceList.length" description="\u6682\u65e0\u5f55\u5165\u4eba\u8138，\u8bf7\u8054\u7cfb\u7ba1\u7406\u5458\u5f55\u5165" \/>
\n <\/div>
\n <\/div>
\n<\/template><\/p>\n

<script setup>
\n\/** \u6211\u7684\u4eba\u8138\u9875\u9762（\u666e\u901a\u7528\u6237） *\/
\nimport { ref, onMounted } from 'vue'
\nimport { faceApi } from '@\/api'<\/p>\n

const faceList = ref([])
\nonMounted(async () => {
\n const res = await faceApi.list()
\n faceList.value = res.data
\n})
\n<\/script><\/p>\n

<style scoped>
\n.face-grid { display: flex; flex-wrap: wrap; gap: 20px; }
\n.face-card { background: #fff; padding: 16px; border-radius: 12px; box-shadow: 0 2px 12px rgba(0,0,0,0.06); text-align: center; }
\n.face-info { margin-top: 8px; display: flex; flex-direction: column; gap: 4px; font-size: 13px; color: #909399; }
\n<\/style><\/p>\n","protected":false},"excerpt":{"rendered":"

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