HarmonyOS鸿蒙Next中flutter项目插件适配: google_mlkit_face_detection / 或有什么替代方案

开发者你好，可以参考下以下方案通过FlutterChannel（链接来自gitcode）实现调用基础视觉服务人脸检测能力进行图片中人脸检测，实现步骤如下：

1. Flutter侧通过ImagePicker拉起系统相册，返回XFile：

  XFile? _image;
  Future<void> _selectPhoto() async {
    print('>>> enter _selectPhoto');
    final picker = ImagePicker();
    final image = await picker.pickImage(
      source: ImageSource.gallery,
      maxWidth: 800,
    );
    setState(() => _image = image);
    print('chooseImage is: ${_image?.path}');
    await _sendToOhos();
  }

1. 通过MethodChannel把图片本地路径发送到HarmonyOS next侧：

await _channel.invokeMethod('sendImagePath', {'path': _image!.path});

1. 在HarmonyOS next侧使用人脸检测能力获取到人脸信息：

  private startDetect(): Promise<string> {
    return new Promise((resolve, reject) => {
      if (!this.chooseImage) {
        reject('No image');
        return;
      }
      faceDetector.init();
      let visionInfo: faceDetector.VisionInfo = {
        pixelMap: this.chooseImage
      };
      faceDetector.detect(visionInfo)
        .then((faces: faceDetector.Face[]) => {
          faceDetector.release();
          let json = faces.length === 0
            ? 'No face is detected in the image.'
            : JSON.stringify(faces);
          resolve(json);
        })
        .catch((err: BusinessError) => {
          faceDetector.release();
          reject(err.message);
        });
    });
  }

1. 将人脸信息通过MethodChannel回传Flutter侧：

        let detectJson = await this.startDetect();
        hilog.info(0x0000, TAG, `detectJson data is: ${detectJson}`);
        result.success(detectJson);

完整示例如下：

face.dart：

import 'dart:io';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:image_picker/image_picker.dart';

class FaceDetectPage extends StatefulWidget {
  const FaceDetectPage({super.key});
  @override
  _FaceDetectPageState createState() => _FaceDetectPageState();
}

class _FaceDetectPageState extends State<FaceDetectPage> {
  final MethodChannel _channel = const MethodChannel('FaceDetectChannel');
  XFile? _image;
  late String detectResult = '等待检测…';

  Future<void> _selectPhoto() async {
    print('>>> enter _selectPhoto');
    final picker = ImagePicker();
    final image = await picker.pickImage(
      source: ImageSource.gallery,
      maxWidth: 800,
    );
    setState(() => _image = image);
    print('chooseImage is: ${_image?.path}');
    await _sendToOhos();
  }

  Widget _imageArea() {
    return Container(
      width: 300,
      height: 300,
      decoration: BoxDecoration(
        border: Border.all(color: Colors.grey),
        borderRadius: BorderRadius.circular(8),
        image: DecorationImage(
          image: _image != null
              ? FileImage(File(_image!.path))
              : const NetworkImage(''),
          fit: BoxFit.cover,
        ),
      ),
    );
  }

  Widget _buttons() {
    return Column(
      mainAxisSize: MainAxisSize.min,
      children: [
        ElevatedButton(
          onPressed: _selectPhoto,
          child: const Text('选择图片'),
        ),
        const SizedBox(height: 10),
        ElevatedButton(
          onPressed: _image == null ? null : _startDetect,
          child: const Text('开始检测'),
        ),
      ],
    );
  }

  Future<void> _sendToOhos() async {
    if (_image == null) return;
    await _channel.invokeMethod('sendImagePath', {'path': _image!.path});
    ScaffoldMessenger.of(context).showSnackBar(
      const SnackBar(content: Text('已发送')),
    );
  }

  Future<void> _startDetect() async {
    if (_image == null) return;

    final String result = await _channel.invokeMethod('startDetect');
    print('detectResult is: $result');

    setState(() {
      detectResult = result;
    });

    ScaffoldMessenger.of(context).showSnackBar(
      const SnackBar(content: Text('detect success')),
    );
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(title: const Text('face detect')),
      body: Center(
        child: Column(
          mainAxisAlignment: MainAxisAlignment.start,
          children: [
            _imageArea(),
            const SizedBox(height: 20),
            Container(
              width: 300,
              height: 200,
              alignment: Alignment.centerLeft,
              child: Text(
                'data is: $detectResult',
                maxLines: 8,
                overflow: TextOverflow.ellipsis,
                softWrap: true,
                style: const TextStyle(fontSize: 14),
              ),
            ),
            _buttons(),
          ],
        ),
      ),
    );
  }
}

FaceDetectPlugin.ets：

import {
  FlutterPlugin,
  FlutterPluginBinding
} from '@ohos/flutter_ohos/src/main/ets/embedding/engine/plugins/FlutterPlugin';
import MethodChannel, {
  MethodCallHandler,
  MethodResult
} from '@ohos/flutter_ohos/src/main/ets/plugin/common/MethodChannel';
import MethodCall from '@ohos/flutter_ohos/src/main/ets/plugin/common/MethodCall';
import Log from '@ohos/flutter_ohos/src/main/ets/util/Log';
import { faceDetector } from '@kit.CoreVisionKit';
import { image } from '@kit.ImageKit';
import { hilog } from '@kit.PerformanceAnalysisKit';
import { BusinessError } from '@kit.BasicServicesKit';
import { fileIo } from '@kit.CoreFileKit';

let TAG = 'FaceDetectPlugin';

export class FaceDetectPlugin implements FlutterPlugin, MethodCallHandler {
  private chooseImage: PixelMap | undefined = undefined;
  private mMethodChannel: MethodChannel | null = null;

  getUniqueClassName(): string {
    return TAG;
  }

  onAttachedToEngine(binding: FlutterPluginBinding): void {
    Log.i(TAG, 'FaceDetectPlugin onAttachedToEngine');
    this.mMethodChannel = new MethodChannel(binding.getBinaryMessenger(), 'FaceDetectChannel');
    this.mMethodChannel.setMethodCallHandler(this);
  }

  onDetachedFromEngine(): void {
    this.mMethodChannel = null;
  }

  async onMethodCall(call: MethodCall, result: MethodResult): Promise<void> {
    let method: string = call.method;
    Log.i(TAG, `Received ${method} message.`);
    Log.i(TAG, `Received ${call.argument('path')} message.`);
    switch (method) {
      case 'sendImagePath':
        try {
          this.chooseImage = await this.loadImage(call.argument('path'));
          hilog.info(0x0000, TAG, 'end chooseImage, can start detect');
        } catch (e) {
          hilog.error(0x0000, TAG, `chooseImage error: ${e}`);
        }
        break;
      case 'startDetect':
        if (!this.chooseImage) {
          result.error('NO_IMAGE', 'please chooseImage first', null);
          return;
        }
        let detectJson = await this.startDetect();
        hilog.info(0x0000, TAG, `detectJson data is: ${detectJson}`);
        result.success(detectJson);
        break;
    }
  }

  private async loadImage(path: string): Promise<image.PixelMap> {
    hilog.info(0x0000, TAG, `start loadImage: ${path}`);
    let file = await fileIo.open(path, fileIo.OpenMode.READ_ONLY);
    try {
      let source = image.createImageSource(file.fd);
      let pixelMap = await source.createPixelMap();
      hilog.info(0x0000, TAG, 'createPixelMap success');
      return pixelMap;
    } finally {
      await fileIo.close(file);
    }
  }

  private startDetect(): Promise<string> {
    return new Promise((resolve, reject) => {
      if (!this.chooseImage) {
        reject('No image');
        return;
      }
      faceDetector.init();
      let visionInfo: faceDetector.VisionInfo = {
        pixelMap: this.chooseImage
      };
      faceDetector.detect(visionInfo)
        .then((faces: faceDetector.Face[]) => {
          faceDetector.release();
          let json = faces.length === 0
            ? 'No face is detected in the image.'
            : JSON.stringify(faces);
          resolve(json);
        })
        .catch((err: BusinessError) => {
          faceDetector.release();
          reject(err.message);
        });
    });
  }
}