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套件詳情v0.23.1

google_ml_kit

一個用於行動平台的 Flutter 插件,可使用 Google 單獨的 ML Kit 所有 API。

AndroidiOS
近 30 天下載
3930
喜歡數
1200
Pub 分數
160

安裝套件

flutter pub add google_ml_kit

套件介紹

根據套件公開資料產生的選型評估,僅供參考。

Google ML Kit 是一個 Flutter 插件,可提供對 Google 的 ML Kit API 的存取,適用於行動平台,讓開發者能夠將機器學習功能(如影像標記、人臉偵測、文字辨識和文件掃描)直接整合至其應用程式中。它支援 Android 和 iOS,提供針對裝置端處理優化的預訓練模型。此外,該插件無需雲端連線即可簡化強大電腦視覺與 OCR 功能的使用。透過直覺的 API,可快速實作智慧功能,例如條碼掃描、地標偵測以及從影像中擷取文字。

適用情境

  • 即時影像文字辨識
  • 文件掃描和提取
  • 面部和關鍵點檢測
  • 條碼和二維碼掃描
  • 圖像標註和分類

主要特色

  • 設備端機器學習
  • 對 Android 和 iOS 的支援
  • 預訓練模型用於常見任務
  • 與 Flutter 的輕鬆整合
  • 低延遲推理

適合對象

  • 需要OCR的移動應用
  • 需要即時影像分析的應用程式
  • 開發文件掃描儀的開發者
  • 使用設備端人工智慧的項目
  • 專注於電腦視覺的Flutter應用

使用建議

  • 需要原生SDK設定才能實現完整功能
  • 模型大小可能影響應用程式封裝大小
  • 性能取決於設備硬體
  • 對高階機器學習模型的自訂有限
  • 可能需要額外的權限以存取攝影機

原始 README

以下為關聯原始碼儲存庫的 README 原文快照,最新內容請造訪原始碼儲存庫。

Google's ML Kit for Flutter

Google's ML Kit for Flutter is a set of Flutter plugins that enable Flutter apps to use Google's standalone ML Kit.

Features

Vision APIs
Feature Plugin Source Code Android iOS
Barcode Scanning google_mlkit_barcode_scanning Pub Version GitHub ✅ ✅
Face Detection google_mlkit_face_detection Pub Version GitHub ✅ ✅
Face Mesh Detection (Beta) google_mlkit_face_mesh_detection Pub Version GitHub ✅ ❌
Text Recognition v2 google_mlkit_text_recognition Pub Version GitHub ✅ ✅
Image Labeling google_mlkit_image_labeling Pub Version GitHub ✅ ✅
Object Detection and Tracking google_mlkit_object_detection Pub Version GitHub ✅ ✅
Digital Ink Recognition google_mlkit_digital_ink_recognition Pub Version GitHub ✅ ✅
Pose Detection (Beta) google_mlkit_pose_detection Pub Version GitHub ✅ ✅
Selfie Segmentation (Beta) google_mlkit_selfie_segmentation Pub Version GitHub ✅ ✅
Subject Segmentation (Beta) google_mlkit_subject_segmentation Pub Version GitHub ✅ ❌
Document Scanner google_mlkit_document_scanner Pub Version GitHub ✅ ❌
Natural Language APIs
Feature Plugin Source Code Android iOS
Language Identification google_mlkit_language_id Pub Version GitHub ✅ ✅
On-Device Translation google_mlkit_translation Pub Version GitHub ✅ ✅
Smart Reply google_mlkit_smart_reply Pub Version GitHub ✅ ✅
Entity Extraction (Beta) google_mlkit_entity_extraction Pub Version GitHub ✅ ✅
GenAI APIs

⚠️ Important: GenAI APIs are built on top of AICore and will not support all Android devices. These APIs require devices with AICore support. Please check device compatibility before using these features in production.

⚠️ Production Disclaimer: Using GenAI plugins in production is the responsibility of the developers consuming the plugins, not the authors. The authors provide these plugins as-is and are not responsible for any issues, failures, or compatibility problems that may arise from using these plugins in production environments.

Feature Plugin Source Code Android iOS
Summarization (Beta) google_mlkit_genai_summarization Pub Version GitHub ✅ ❌
Proofreading (Beta) google_mlkit_genai_proofreading Pub Version GitHub ✅ ❌
Rewriting (Beta) google_mlkit_genai_rewriting Pub Version GitHub ✅ ❌
Image Description (Beta) google_mlkit_genai_image_description Pub Version GitHub ✅ ❌
Speech Recognition (Alpha) google_mlkit_genai_speech_recognition Pub Version GitHub ✅ ❌
Prompt (Beta) google_mlkit_genai_prompt Pub Version GitHub ✅ ❌

PLEASE READ THIS before continuing or posting a new issue:

  • Google's ML Kit was build only for mobile platforms: iOS and Android apps. Web or any other platform is not supported, you can request support for those platform to Google in their repo.

  • This plugin is not sponsored or maintained by Google. The authors are developers excited about Machine Learning that wanted to expose Google's native APIs to Flutter.

  • Google's ML Kit APIs are only developed natively for iOS and Android. This plugin uses Flutter Platform Channels as explained here.

    Messages are passed between the client (the app/plugin) and host (platform) using platform channels as illustrated in this diagram:

    Messages and responses are passed asynchronously, to ensure the user interface remains responsive. To read more about platform channels go here.

    Because this plugin uses platform channels, no Machine Learning processing is done in Flutter/Dart, all the calls are passed to the native platform using MethodChannel in Android and FlutterMethodChannel in iOS, and executed using Google's native APIs. Think of this plugin as a bridge between your app and Google's native ML Kit APIs. This plugin only passes the call to the native API and the processing is done by Google's API. It is important that you understand this concept when it comes to debugging errors for your ML model and/or app.

  • Since the plugin uses platform channels, you may encounter issues with the native API. Before submitting a new issue, identify the source of the issue. You can run both iOS and/or Android native example apps by Google and make sure that the issue is not reproducible with their native examples. If you can reproduce the issue in their apps then report the issue to Google. The authors do not have access to the source code of their native APIs, so you need to report the issue to them. If you find that their example apps are okay and still you have an issue using this plugin, then look at our closed and open issues. If you cannot find anything that can help you then report the issue and provide enough details. Be patient, someone from the community will eventually help you.

Migrating from ML Kit for Firebase to the new standalone ML Kit SDK

When Migrating from ML Kit for Firebase to the new standalone ML Kit SDK read this guide.

For Android details read this.

For iOS details read this.

Example app

Find the example app here.

Consuming image streams on Android

Until https://github.com/flutter/flutter/issues/145961 is resolved, the most recent version of the camera package doesn't support the required image formats. To make things work, manually add the legacy plugin via the instructions from https://pub.dev/packages/camera_android.

Contributing

Contributions are welcome. In case of any problems look at existing issues, if you cannot find anything related to your problem then open an issue. Create an issue before opening a pull request for non trivial fixes. In case of trivial fixes open a pull request directly.

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原始碼儲存庫

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