视频中的面部识别（抖动）

Question

我收到了一个进行面部识别的项目，该项目由用户录制在视频中，以避免欺诈。用户录制了一个+/- 5秒的视频，该代码取了他的脸，并将其发送到银行。事实证明，我的代码给出了一个非常奇怪的错误。该代码从库中获取视频，但将其保存为.jpg，并且在您按下“扫描”时出现此错误：

E/flutter (10653): [ERROR:flutter/lib/ui/ui_dart_state.cc(166)] Unhandled Exception: 'package:firebase_ml_vision/src/firebase_vision.dart': Failed assertion: line 111 pos 12: 'imageFile != null': is not true.

我四处张望，却找不到任何帮助。

import 'dart:io';
import 'package:flutter/material.dart';
import 'package:file_picker/file_picker.dart';
import 'package:image_picker/image_picker.dart';
import 'package:firebase_ml_vision/firebase_ml_vision.dart';
import 'package:flutter_app_face/face_detector_controller.dart';

class MyFaceDetectorView extends StatefulWidget {
  @override
  _MyFaceDetectorViewState createState() => _MyFaceDetectorViewState();
}

class _MyFaceDetectorViewState extends State<MyFaceDetectorView> {
  final _picker = ImagePicker();
  File _videoFile;

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        title: Text(
          "Face Detector",
          style: TextStyle(fontSize: 30),
        ),
        centerTitle: true,
      ),
      body: myFaceView(),
    );
  }

  myFaceView() {
    return SingleChildScrollView(
      child: Container(
        child: Column(
          children: <Widget>[
            Container(
              child: Container(
                  child: Column(
                children: [
                  Padding(
                    padding: EdgeInsets.all(8.0),
                    child: Text(
                      "Select a file",
                      textAlign: TextAlign.center,
                      style: TextStyle(fontSize: 20),
                    ),
                  ),
                  Row(
                    mainAxisAlignment: MainAxisAlignment.center,
                    children: [
                      Padding(
                        padding: EdgeInsets.all(10),
                        child: CircleAvatar(
                          child: IconButton(
                            icon: Icon(Icons.video_library),
                            onPressed: () async => await pickVideo(),
                          ),
                        ),
                      ),
                    ],
                  ),
                  Padding(
                    padding: EdgeInsets.only(bottom: 18),
                    child: RaisedButton(
                      child: Text("Scan", style: TextStyle(fontSize: 16)),
                      onPressed: scanner,
                      color: Colors.orange,
                    ),
                  ),
                ],
              )),
            ),
          ],
        ),
      ),
    );
  }

  scanner() {
    List<Face> faces;

    if (faces != null) {
      return _alert();
    } else {
      FaceDetectorController faceDetec = FaceDetectorController();
      faceDetec.pickVideo(_videoFile);
      print("scanned");
    }
  }

  Future<void> _alert() {
    return showDialog<void>(
      context: context,
      builder: (BuildContext context) {
        return AlertDialog(
          title: Text('No faces found'),
          content: const Text(
              'We didn't find anything in your file, please try again.'),
          actions: <Widget>[
            FlatButton(
              child: Text('Ok'),
              onPressed: () {
                Navigator.of(context).pop();
              },
            ),
          ],
        );
      },
    );
  }
  pickVideo() async {
    final PickedFile videoFile = await _picker.getVideo(
      source: ImageSource.gallery,
      maxDuration: Duration(seconds: 5),
    );

    setState(() {
      this._videoFile = File(videoFile.path);
    });
    print("Finish");
  }
}

控制器：

    import 'dart:io';
    import 'package:firebase_ml_vision/firebase_ml_vision.dart';
    
    class FaceDetectorController {
pickVideo(File video) async {
    final FirebaseVisionImage visionImage = FirebaseVisionImage.fromFile(video);
    print('VisionImage: $video');

    final FaceDetectorOptions faceDetectorOptions = FaceDetectorOptions(
      enableTracking: true,
      enableLandmarks: true,
      enableContours: false,
      enableClassification: true,
      minFaceSize: 0.1,
      mode: FaceDetectorMode.accurate,
    );

    final FaceDetector faceDetector =
        FirebaseVision.instance.faceDetector(faceDetectorOptions);

    faceDetector.processImage(visionImage);

    final List<Face> faces = await faceDetector.processImage(visionImage);
    print('Number of Faces: ${faces.length}');

    for (Face face in faces) {
      var boundingBox = face.boundingBox;
      final double rotY = face.headEulerAngleY;
      final double rotZ = face.headEulerAngleZ;
      print('Face Rotation: $rotY, $rotZ');

      if (face.trackingId != null) {
        final int id = face.trackingId;
        print('Face: $id');
      }

      final FaceLandmark leftEar = face.getLandmark(FaceLandmarkType.leftEar);
      if (leftEar != null) {
        var leftEarPos = leftEar.position;
        print('left ear position: ${leftEarPos}');
      }

      if (face.smilingProbability != null) {
        var smileProb = face.smilingProbability;
        print('Smile: $smileProb');
      }

      if (face.leftEyeOpenProbability != null) {
        var leftEyeOpen = face.leftEyeOpenProbability;
        print('Left eye open: $leftEyeOpen');
      }
      if (face.rightEyeOpenProbability != null) {
        var rightEyeOpen = face.rightEyeOpenProbability;
        print('Right eye open: $rightEyeOpen');
      }
    }
  }
}

Answer 1

您是否尝试过python的deepface软件包？它会访问您的网络摄像头并实时应用人脸识别。

  List<Student> _firebaseStudentsFromSnapshot(QuerySnapshot snapshot) {
    return snapshot.documents.map((doc) {
      List<Mark> marks = [];
      List<dynamic> markMap = doc.data['grades'];
      markMap.forEach((element) {
        marks.add(new Mark(
            mark = element['mark'], 
            grade = element['grade'],
            markedBy = element['markedBy'],
            feedback = element['feedback'];
        ));
      });
      return Student(
        name: doc.data['name'] ?? '',
        subject: doc.data['subject'] ?? '',
        marks: marks,
      );
    }).toList();
  }

在这里，您需要将面部图像数据库存储在C：/ my_db文件夹中。