Flutter Google ML Kit 人脸检测未检测到

Question

我正在尝试检测图像上的人脸。 我用相机拍了一张照片。 然后我试图在这张照片上找到面孔。 但是我的 faces 变量始终是 null。我正在使用这个 package进行检测。 这是我的代码片段：

  final faceDetector = GoogleMlKit.vision.faceDetector(FaceDetectorOptions(enableTracking: false, enableContours: false, enableClassification: false, enableLandmarks: false, mode: FaceDetectorMode.accurate ));

  @override
  void initState() {
    super.initState();
    findFaces();
  }

  Future<void> detectFace() async {
    final InputImage data = InputImage.fromFile(widget.capturedPhoto);
    final List<Face> faces = await faceDetector.processImage(data);
  }

capturedPhoto 是包含用相机拍摄的照片的文件。 我的 faces 变量始终是 null 列表。 这里还有一个警告：

[WARNING]The specified colorspace format is not supported. Falling back on Libyuv.

如何解决这个问题？

Answer 1

为此，请检查我的代码。 捕获图像后，我确实将文件传递到此页面。在这里我执行图像检测

import 'dart:async';
import 'dart:io';
import 'dart:math';
import 'package:camera/camera.dart';
import 'package:flutter/material.dart';
import 'package:google_ml_kit/google_ml_kit.dart';

class DetailScreen extends StatefulWidget {
  final String imagePath;

  const DetailScreen({required this.imagePath});

  @override
  _DetailScreenState createState() => _DetailScreenState();
}

class _DetailScreenState extends State<DetailScreen> {
  late final String _imagePath;

 final faceDetector = GoogleMlKit.vision.faceDetector(FaceDetectorOptions(
      enableTracking: true,
      enableContours: true,
      enableClassification: true,
      enableLandmarks: true,
      mode: FaceDetectorMode.accurate));

  List<dynamic> faceValue = [];

  Future<void> _getImageSize(File imageFile) async {
    final Completer<Size> completer = Completer<Size>();

    final Image image = Image.file(imageFile);
    image.image.resolve(const ImageConfiguration()).addListener(
      ImageStreamListener((ImageInfo info, bool _) {
        completer.complete(Size(
          info.image.width.toDouble(),
          info.image.height.toDouble(),
        ));
      }),
    );

    final Size imageSize = await completer.future;
    setState(() {
      _imageSize = imageSize;
    });
  }


  void _recognizeImage() async {
    _getImageSize(File(_imagePath));
    final inputImage = InputImage.fromFilePath(_imagePath);
    final List<Face> faces = await faceDetector.processImage(inputImage);
    for (Face face in faces) {
      final Rect boundingBox = face.boundingBox;
      final double? rotY =
          face.headEulerAngleY; // Head is rotated to the right rotY degrees
      final double? rotZ =
          face.headEulerAngleZ; // Head is tilted sideways rotZ degrees

      final FaceLandmark? leftEar = face.getLandmark(FaceLandmarkType.leftEar);
      if (leftEar != null) {
        final Offset leftEarPos = leftEar.position;
        print("ppppppppppppppppppppppppppppppppppppppppppp ${leftEarPos}");
      }

      if (face.smilingProbability != null) {
        final double? smileProb = face.smilingProbability;
        print("ppppppppppppppppppppppppppppppppppppppppppp ${smileProb}");
      }

      if (face.trackingId != null) {
        final int? id = face.trackingId;

        print("ppppppppppppppppppppppppppppppppppppppppppp ${id}");
      }

      setState(() {
        faceValue.addAll([
          boundingBox,
          rotY,
          rotZ,
          face.trackingId,
          face.smilingProbability,
        ]);
        print(
            "lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll ${faceValue}");
      });
    }
  }

  @override
  void initState() {
    _imagePath = widget.imagePath;
    final faceDetector = GoogleMlKit.vision.faceDetector(FaceDetectorOptions(
        enableTracking: true,
        enableContours: true,
        enableClassification: true,
        enableLandmarks: true,
        mode: FaceDetectorMode.accurate));
    _recognizeImage();
    super.initState();
  }

  @override
  void dispose() {
    // Disposing the imageLabeler when not used anymore
    imageLabeler.close();
    textDetector.close();
    faceDetector.close();
    // objectDetector.close();
    super.dispose();
  }


  @override
  Widget build(BuildContext context) {
    return Scaffold(
      body: _imageSize != null
          ? Stack(
              children: [
                Container(
                  width: double.maxFinite,
                  color: Colors.black,
                  child: AspectRatio(
                    aspectRatio: _imageSize!.aspectRatio,
                    child: Image.file(
                      File(_imagePath),
                    ),
                  ),
                ),
                Align(
                  alignment: Alignment.bottomCenter,
                  child: Container(
                    color: Colors.amber,
                    child: SingleChildScrollView(
                      padding: EdgeInsets.all(20),
                      child: faceValue != null
                          ? ListView.builder(
                              shrinkWrap: true,
                              physics: BouncingScrollPhysics(),
                              itemCount: faceValue.length,
                              itemBuilder: (context, index) {
                                return Column(
                                  children: [
                                    Text(
                                      '${faceValue[index]}',
                                      style: TextStyle(
                                          fontSize: 20,
                                          fontWeight: FontWeight.w900),
                                    )
                                  ],
                                );
                              })
                          : Container(),
                    ),
                  ),
                ),
              ],
            )
          : Container(
              color: Colors.blue,
              child: Center(
                child: CircularProgressIndicator(),
              ),
            ),
    );
  }
}

输出

[Rect.fromLTRB(247.0, 224.0, 554.0, 532.0), 8.070308685302734, 2.248018503189087, 0, 0.9941101670265198,]

Answer 2

这是由于图像方向问题。 原生 ios 相机拍摄的图像，当看起来是纵向时，实际上可能是横向纵向。 所以我通过 Exif 数据检查了真实方向并修复了它们的方向。 现在效果很好。