鸿蒙跨端人脸年龄检测系统开发指南 原创

鸿蒙跨端人脸年龄检测系统开发指南

一、项目概述

本文基于HarmonyOS的AI能力(@ohos.ai)和分布式数据同步技术，开发一款简易人脸年龄检测系统。该系统能够通过设备摄像头实时检测人脸并估算年龄，同时实现多设备间的检测结果同步，借鉴了《鸿蒙跨端U同步》中多设备数据同步的技术原理。

二、系统架构

±--------------------+ ±--------------------+ ±--------------------+
主设备 <-----> 分布式数据总线 <-----> 从设备
(Primary Device) (Distributed Bus) (Secondary Device)
±---------±---------+ ±---------±---------+ ±---------±---------+

±---------v----------+ ±---------v----------+ ±---------v----------+
摄像头采集模块 人脸检测模块 数据同步模块
(Camera Service) (Face Detection) (Data Sync)

±--------------------+ ±--------------------+ ±--------------------+

三、核心代码实现
人脸检测服务

// src/main/ets/service/FaceDetectionService.ts
import { faceDetection } from ‘@ohos.ai.faceDetection’;
import { distributedData } from ‘@ohos.data.distributedData’;
import { BusinessError } from ‘@ohos.base’;
import { camera } from ‘@ohos.multimedia.camera’;
import { image } from ‘@ohos.multimedia.image’;

interface FaceInfo {
faceId: number;
age: number;
confidence: number;
timestamp: number;
position: {
left: number;
top: number;
right: number;
bottom: number;
};
export class FaceDetectionService {

private static instance: FaceDetectionService;
private faceDetector: faceDetection.FaceDetector | null = null;
private kvStore: distributedData.KVStore | null = null;
private readonly STORE_ID = ‘face_data_store’;
private cameraInput: camera.CameraInput | null = null;
private previewOutput: camera.PreviewOutput | null = null;
private faceInfos: FaceInfo[] = [];

private constructor() {
this.initFaceDetector();
this.initKVStore();
this.initCamera();
public static getInstance(): FaceDetectionService {

if (!FaceDetectionService.instance) {
  FaceDetectionService.instance = new FaceDetectionService();

return FaceDetectionService.instance;

private async initFaceDetector(): Promise<void> {

try {
  this.faceDetector = faceDetection.createFaceDetector();
  const config: faceDetection.FaceDetectionConfig = {
    age: true, // 启用年龄检测
    featurePoints: false,
    facialExpression: false,
    gender: false,
    hat: false,
    glasses: false
  };
  await this.faceDetector.init(config);

catch (e) {

  console.error(Failed to initialize face detector. Code: {e.code}, message: {e.message});

}

private async initKVStore(): Promise<void> {
try {
const options: distributedData.KVManagerConfig = {
bundleName: ‘com.example.facedetection’,
userInfo: {
userId: ‘0’,
userType: distributedData.UserType.SAME_USER_ID
};

  const kvManager = distributedData.createKVManager(options);
  this.kvStore = await kvManager.getKVStore({
    storeId: this.STORE_ID,
    options: {
      createIfMissing: true,
      encrypt: false,
      backup: false,
      autoSync: true,
      kvStoreType: distributedData.KVStoreType.SINGLE_VERSION

});

  // 注册数据变化监听
  this.kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_REMOTE, (data) => {
    data.insertEntries.forEach((entry: distributedData.Entry) => {
      if (entry.key === 'face_infos') {
        this.notifyFaceInfoChange(entry.value.value as FaceInfo[]);

});

  });

catch (e) {

  console.error(Failed to initialize KVStore. Code: {e.code}, message: {e.message});

}

private async initCamera(): Promise<void> {
try {
const cameraManager = camera.getCameraManager();
const cameras = cameraManager.getSupportedCameras();
if (cameras.length === 0) {
console.error(‘No camera available’);
return;
// 使用后置摄像头

  this.cameraInput = cameraManager.createCameraInput(cameras[0]);
  await this.cameraInput.open();
  
  // 创建预览输出
  const surfaceId = 'previewSurface';
  this.previewOutput = cameraManager.createPreviewOutput(surfaceId);
  
  // 设置帧回调
  this.previewOutput.on('frameStart', () => {
    this.onFrame();
  });
  
  // 创建会话并开始预览
  const captureSession = cameraManager.createCaptureSession();
  await captureSession.beginConfig();
  await captureSession.addInput(this.cameraInput);
  await captureSession.addOutput(this.previewOutput);
  await captureSession.commitConfig();
  await captureSession.start();

catch (e) {

  console.error(Failed to initialize camera. Code: {e.code}, message: {e.message});

}

private async onFrame(): Promise<void> {
if (!this.faceDetector || !this.previewOutput) return;

try {
  const imageObj = await this.previewOutput.getFrame();
  const detectionResult = await this.faceDetector.detect(imageObj);
  
  const newFaceInfos: FaceInfo[] = detectionResult.faces.map((face, index) => ({
    faceId: index,
    age: face.age || 0,
    confidence: face.confidence || 0,
    timestamp: Date.now(),
    position: {
      left: face.rect.left,
      top: face.rect.top,
      right: face.rect.right,
      bottom: face.rect.bottom

}));

  this.faceInfos = newFaceInfos;
  
  // 同步到其他设备
  if (this.kvStore) {
    await this.kvStore.put('face_infos', { value: newFaceInfos });

// 释放图像资源

  imageObj.release();

catch (e) {

  console.error(Failed to process frame. Code: {e.code}, message: {e.message});

}

private notifyFaceInfoChange(infos: FaceInfo[]): void {
// 实际应用中这里应该通知UI更新
console.log(‘Face infos updated:’, infos);
this.faceInfos = infos;
public async getCurrentFaceInfos(): Promise<FaceInfo[]> {

if (!this.kvStore) return [];

try {
  const entry = await this.kvStore.get('face_infos');
  return entry?.value || [];

catch (e) {

  console.error(Failed to get face infos. Code: {e.code}, message: {e.message});
  return [];

}

public async destroy(): Promise<void> {
if (this.faceDetector) {
await this.faceDetector.release();
if (this.kvStore) {

  this.kvStore.off('dataChange');

if (this.cameraInput) {

  await this.cameraInput.close();

}

人脸检测组件

// src/main/ets/components/FaceDetectionView.ets
@Component
export struct FaceDetectionView {
private faceService = FaceDetectionService.getInstance();
@State faceInfos: FaceInfo[] = [];
@State previewSurfaceId: string = ‘previewSurface’;
@State showAge: boolean = true;

aboutToAppear(): void {
this.loadFaceInfos();
private async loadFaceInfos(): Promise<void> {

this.faceInfos = await this.faceService.getCurrentFaceInfos();

build() {

Stack() {
  // 摄像头预览
  CameraPreview({ surfaceId: this.previewSurfaceId })
    .width('100%')
    .height('100%');
  
  // 人脸框和年龄显示
  ForEach(this.faceInfos, (face) => {
    Column() {
      if (this.showAge) {
        Text(${face.age}岁)
          .fontSize(16)
          .fontColor('#FFFFFF')
          .backgroundColor('#88000000')
          .borderRadius(10)
          .padding(5)
          .margin({ bottom: 5 });

// 人脸框

      Rect()
        .width(face.position.right - face.position.left)
        .height(face.position.bottom - face.position.top)
        .position({ x: face.position.left, y: face.position.top })
        .border({ width: 2, color: '#FF4081' })
        .borderRadius(5);

.position({ x: face.position.left, y: face.position.top })

  })

.width(‘100%’)

.height('100%')
.onAppear(() => {
  this.faceService.getCurrentFaceInfos().then((infos) => {
    this.faceInfos = infos;
  });
});

}

主界面实现

// src/main/ets/pages/FaceDetectionPage.ets
import { FaceDetectionService } from ‘…/service/FaceDetectionService’;
import { FaceDetectionView } from ‘…/components/FaceDetectionView’;

@Entry
@Component
struct FaceDetectionPage {
@State showAge: boolean = true;
@State deviceList: string[] = [];
private faceService = FaceDetectionService.getInstance();

build() {
Column() {
// 标题
Text(‘跨端人脸年龄检测’)
.fontSize(24)
.fontWeight(FontWeight.Bold)
.margin({ bottom: 20 });

  // 检测视图
  FaceDetectionView()
    .width('90%')
    .height('60%')
    .margin({ bottom: 20 });
  
  // 控制面板
  Row() {
    Toggle({ type: ToggleType.Checkbox, isOn: this.showAge })
      .onChange((isOn: boolean) => {
        this.showAge = isOn;
      })
      .margin({ right: 10 });
    
    Text('显示年龄')
      .fontSize(16);

.justifyContent(FlexAlign.Start)

  .width('90%')
  .margin({ bottom: 20 });
  
  // 设备列表
  if (this.deviceList.length > 0) {
    Column() {
      Text('已连接设备')
        .fontSize(18)
        .fontWeight(FontWeight.Bold)
        .margin({ bottom: 10 });
      
      ForEach(this.deviceList, (device) => {
        Text(device)
          .fontSize(14)
          .margin({ bottom: 5 });
      })

.width(‘90%’)

    .padding(15)
    .backgroundColor('#FFFFFF')
    .borderRadius(10)
    .shadow({ radius: 5, color: '#E0E0E0', offsetX: 0, offsetY: 2 });

}

.width('100%')
.height('100%')
.padding(20)
.backgroundColor('#F5F5F5')
.onAppear(() => {
  // 模拟获取设备列表
  setTimeout(() => {
    this.deviceList = ['设备1', '设备2'];
  }, 1000);
});

}

四、与游戏同步技术的结合点
分布式数据同步：借鉴游戏中多设备玩家数据同步机制，实现人脸检测结果的跨设备同步

实时更新机制：类似游戏中的实时状态更新，确保多设备间检测结果的一致性

设备识别：类似游戏中玩家设备识别，标记检测结果的来源设备

冲突解决策略：使用时间戳优先策略解决多设备同时更新检测结果的冲突

数据压缩传输：优化人脸检测数据的传输效率，类似游戏中的网络优化

五、关键特性实现
人脸检测与年龄估计：

  const detectionResult = await this.faceDetector.detect(imageObj);

const newFaceInfos: FaceInfo[] = detectionResult.faces.map((face, index) => ({
faceId: index,
age: face.age || 0,
confidence: face.confidence || 0,
timestamp: Date.now(),
position: {
left: face.rect.left,
top: face.rect.top,
right: face.rect.right,
bottom: face.rect.bottom
}));

检测结果同步：

  if (this.kvStore) {
 await this.kvStore.put('face_infos', { value: newFaceInfos });

摄像头帧处理：

  this.previewOutput.on('frameStart', () => {
 this.onFrame();

});

分布式数据监听：

  this.kvStore.on('dataChange', distributedData.SubscribeType.SUBSCRIBE_TYPE_REMOTE, (data) => {
 data.insertEntries.forEach((entry: distributedData.Entry) => {
   if (entry.key === 'face_infos') {
     this.notifyFaceInfoChange(entry.value.value as FaceInfo[]);

});

});

六、性能优化策略
帧率控制：

  // 可以设置适当的帧率，避免处理每一帧

const targetFps = 10;
let lastProcessTime = 0;

if (Date.now() - lastProcessTime < 1000 / targetFps) return;
lastProcessTime = Date.now();

数据压缩传输：

  // 只传输必要的数据，减少数据量

const compressedInfos = newFaceInfos.map(info => ({
a: info.age,
c: info.confidence,
p: [info.position.left, info.position.top, info.position.right, info.position.bottom]
}));

模型轻量化：

  // 使用轻量级模型配置

const config: faceDetection.FaceDetectionConfig = {
age: true,
featurePoints: false, // 关闭不需要的特征点检测
facialExpression: false,
gender: false
};

资源释放管理：

  public async destroy(): Promise<void> {
 if (this.faceDetector) {
   await this.faceDetector.release();

if (this.cameraInput) {

   await this.cameraInput.close();

}

七、项目扩展方向
多人脸跟踪：实现多个人脸的持续跟踪和识别

表情识别：扩展系统以识别面部表情

性别识别：增加性别检测功能

历史数据分析：记录和分析历史检测数据

隐私保护：增加本地处理选项，避免敏感数据传输

八、总结

本简易人脸年龄检测系统实现了以下核心功能：
基于HarmonyOS AI能力的人脸检测和年龄估计

实时摄像头帧处理和结果显示

检测结果的分布式同步

直观的检测结果显示界面

通过借鉴游戏中的多设备同步技术，我们构建了一个高效、实时的人脸检测系统。该项目展示了HarmonyOS在AI能力和分布式技术方面的强大功能，为开发者提供了人脸检测应用开发的参考方案。