搜尋套件、儲存庫與 APP 分類

套件詳情v0.13.0

google_mlkit_commons

一個 Flutter 插件,包含用於在移動平台實現 Google 的獨立 ML Kit 的通用檔案。

AndroidiOS
近 30 天下載
63.1萬
喜歡數
38
Pub 分數
150

安裝套件

flutter pub add google_mlkit_commons

套件介紹

根據套件公開資料產生的選型評估,僅供參考。

一個 Flutter 外掛程式,提供用於將 Google 的獨立 ML Kit 集成到行動應用程式中的共用實用工具和常見元件。它支援 Android 與 iOS 上的機器學習任務,讓開發者能夠利用預先訓練的模型來實現文字辨識、人臉偵測與條碼掃描等功能。該套件透過提供可重複使用的程式碼和跨平台的一致介面,簡化了實作過程。專為與 Flutter 生態系統中的其他 Google ML Kit 外掛程式無縫整合而設計。

適用情境

  • 圖像中的文字識別
  • 條碼掃描
  • 人臉檢測
  • 圖像標註
  • 目標檢測
  • 即時處理

主要特色

  • 共享實用程序類
  • 跨平台相容性
  • 與 Google ML Kit 的整合
  • 輕量級核心依賴項
  • 一致的 API 設計
  • 對 Android 和 iOS 的支援

適合對象

  • 使用 Flutter 的移動應用開發者
  • 需要設備端機器學習推論的專案
  • 需要OCR或電腦視覺的應用
  • 開發人工智慧功能的開發者
  • 使用 Google ML Kit 進行移動開發的團隊

使用建議

  • 需要額外的 ML Kit 插件以啟用特定功能
  • 僅限行動平台
  • 依賴 Google Play 服務(Android)
  • 可能增加應用大小
  • 需要相機和儲存的正確權限
  • 不適用於伺服器端處理

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.

所屬分類

原始碼儲存庫

使用此套件的專案