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套件詳情v0.15.1

google_mlkit_face_detection

一個 Flutter 插件,用於使用 Google 的 ML Kit 人臉偵測功能在影像中偵測人臉、識別關鍵面部特徵並取得偵測到的人臉輪廓。

AndroidiOS
近 30 天下載
13.1萬
喜歡數
329
Pub 分數
150

安裝套件

flutter pub add google_mlkit_face_detection

套件介紹

根據套件公開資料產生的選型評估,僅供參考。

google_mlkit_face_detection 套件是一個 Flutter 插件,利用 Google 的 ML Kit 在影像中偵測人臉,識別眼睛、鼻子和嘴巴等關鍵面部特徵,並提取臉部輪廓。它能在 Android 和 iOS 平台上實現高準確度的即時人臉偵測。專為希望在行動應用程式中整合電腦視覺功能的開發者設計,支援裝置端處理,以確保隱私與效能。此套件是更廣泛的 Flutter ML Kit 生態系統的一部分,可與其他 ML 工具無縫整合。

適用情境

  • 即時人臉偵測在相機應用中
  • 用於AR效果的面部特徵追蹤
  • 透過人臉辨識進行使用者驗證
  • 用於無障礙工具的圖像分析
  • 自動照片標記

主要特色

  • 設備端人臉檢測
  • 支援面部特徵點識別
  • 返回面部輪廓和位置
  • 針對行動效能進行優化
  • 適用於 Android 和 iOS

適合對象

  • 行動應用開發者
  • AR/VR 應用創作者
  • 可存取性工具建構者
  • 注重隱私的圖像處理

使用建議

  • 需要具備足夠處理能力的設備
  • 僅限面部檢測,不支援完整的臉部辨識
  • 可能需要對低品質圖像進行額外預處理
  • 依賴 Android 上的 Google Play 服務

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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