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google_ml_vision

用于 Google ML Kit 本地视觉 API 的 Flutter 插件。它包含条形码扫描、图像标记、文本识别和人脸检测。

AndroidiOS
近 30 天下载
88
喜欢数
135
Pub 分数
140

安装插件

flutter pub add google_ml_vision

插件介绍

基于插件公开信息、社区数据与项目使用情况整理的选型参考。

一个 Flutter 插件,集成 Google ML Kit 的设备端视觉 API,可在 Flutter 应用中直接实现条形码扫描、图像标记、文本识别(OCR)和人脸检测。专为低延迟的实时处理设计,在 Android 和 iOS 设备上本地运行,确保隐私和性能。该插件通过简单的方法简化了常见计算机视觉任务的实现,非常适合需要智能图像分析但不依赖云服务的移动应用。

适合场景

  • 零售应用中的条形码扫描
  • 从图像中提取文本
  • 照片应用中的图像分类
  • 增强现实中的面部检测
  • 文档扫描和OCR

主要特点

  • 设备端处理
  • 支持多种视觉任务
  • 低延迟性能
  • Privacy-preserving
  • 简单 API 集成

适合对象

  • 需要实时视觉功能的移动应用
  • 注重用户隐私的开发者
  • 需要离线功能的应用程序
  • 初创公司正在构建文档或AR工具
  • 使用原生机器学习功能的Flutter开发人员

使用建议

  • 需要 Android 5.0+ 或 iOS 10.0+
  • 仅限支持的视觉模型
  • 无基于云的增强功能
  • 性能因设备而异
  • iOS 可能需要手动配置

原始 README

以下为关联源码仓库的 README 原文快照,最新内容请访问源码仓库。

Google ML Kit Vision Plugin

(https://pub.dev/packages/google_ml_vision)

A Flutter plugin to use the capabilities of on-device Google ML Kit Vision APIs

Usage

To use this plugin, add google_ml_vision as a dependency in your pubspec.yaml file.

Using an ML Vision Detector

1. Create a GoogleVisionImage.

Create a GoogleVisionImage object from your image. To create a GoogleVisionImage from an image File object:

final File imageFile = getImageFile();
final GoogleVisionImage visionImage = GoogleVisionImage.fromFile(imageFile);
2. Create an instance of a detector.
final BarcodeDetector barcodeDetector = GoogleVision.instance.barcodeDetector();
final FaceDetector faceDetector = GoogleVision.instance.faceDetector();
final ImageLabeler labeler = GoogleVision.instance.imageLabeler();
final TextRecognizer textRecognizer = GoogleVision.instance.textRecognizer();

You can also configure all detectors, except TextRecognizer, with desired options.

final ImageLabeler labeler = GoogleVision.instance.imageLabeler(
  ImageLabelerOptions(confidenceThreshold: 0.75),
);
3. Call detectInImage() or processImage() with visionImage.
final List<Barcode> barcodes = await barcodeDetector.detectInImage(visionImage);
final List<Face> faces = await faceDetector.processImage(visionImage);
final List<ImageLabel> labels = await labeler.processImage(visionImage);
final VisionText visionText = await textRecognizer.processImage(visionImage);
4. Extract data.

a. Extract barcodes.

for (Barcode barcode in barcodes) {
  final Rectangle<int> boundingBox = barcode.boundingBox;
  final List<Point<int>> cornerPoints = barcode.cornerPoints;

  final String rawValue = barcode.rawValue;

  final BarcodeValueType valueType = barcode.valueType;

  // See API reference for complete list of supported types
  switch (valueType) {
    case BarcodeValueType.wifi:
      final String ssid = barcode.wifi.ssid;
      final String password = barcode.wifi.password;
      final BarcodeWiFiEncryptionType type = barcode.wifi.encryptionType;
      break;
    case BarcodeValueType.url:
      final String title = barcode.url.title;
      final String url = barcode.url.url;
      break;
  }
}

b. Extract faces.

for (Face face in faces) {
  final Rectangle<int> 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

  // If landmark detection was enabled with FaceDetectorOptions (mouth, ears,
  // eyes, cheeks, and nose available):
  final FaceLandmark leftEar = face.getLandmark(FaceLandmarkType.leftEar);
  if (leftEar != null) {
    final Point<double> leftEarPos = leftEar.position;
  }

  // If classification was enabled with FaceDetectorOptions:
  if (face.smilingProbability != null) {
    final double smileProb = face.smilingProbability;
  }

  // If face tracking was enabled with FaceDetectorOptions:
  if (face.trackingId != null) {
    final int id = face.trackingId;
  }
}

c. Extract labels.

for (ImageLabel label in labels) {
  final String text = label.text;
  final String entityId = label.entityId;
  final double confidence = label.confidence;
}

d. Extract text.

String text = visionText.text;
for (TextBlock block in visionText.blocks) {
  final Rect boundingBox = block.boundingBox;
  final List<Offset> cornerPoints = block.cornerPoints;
  final String text = block.text;
  final List<RecognizedLanguage> languages = block.recognizedLanguages;

  for (TextLine line in block.lines) {
    // Same getters as TextBlock
    for (TextElement element in line.elements) {
      // Same getters as TextBlock
    }
  }
}
5. Release resources with close().
barcodeDetector.close();
faceDetector.close();
labeler.close();
textRecognizer.close();

Getting Started

See the example directory for a complete sample app using Google Machine Learning.

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