mediapipe README

The project's README from github.com/google-ai-edge/mediapipe. As one markdown file: README.md.

layout: forward target: https://developers.google.com/mediapipe title: Home nav_order: 1

Attention: We have moved to https://developers.google.com/mediapipe as the primary developer documentation site for MediaPipe as of April 3, 2023.

MediaPipe

Attention: MediaPipe Solutions Preview is an early release. Learn more.

On-device machine learning for everyone

Delight your customers with innovative machine learning features. MediaPipe contains everything that you need to customize and deploy to mobile (Android, iOS), web, desktop, edge devices, and IoT, effortlessly.

Get started

You can get started with MediaPipe Solutions by by checking out any of the developer guides for vision, text, and audio tasks. If you need help setting up a development environment for use with MediaPipe Tasks, check out the setup guides for Android, web apps, and Python.

Solutions

MediaPipe Solutions provides a suite of libraries and tools for you to quickly apply artificial intelligence (AI) and machine learning (ML) techniques in your applications. You can plug these solutions into your applications immediately, customize them to your needs, and use them across multiple development platforms. MediaPipe Solutions is part of the MediaPipe open source project, so you can further customize the solutions code to meet your application needs.

These libraries and resources provide the core functionality for each MediaPipe Solution:

These tools let you customize and evaluate solutions:

Legacy solutions

We have ended support for these MediaPipe Legacy Solutions as of March 1, 2023. All other MediaPipe Legacy Solutions will be upgraded to a new MediaPipe Solution. See the Solutions guide for details. The code repository and prebuilt binaries for all MediaPipe Legacy Solutions will continue to be provided on an as-is basis.

For more on the legacy solutions, see the documentation.

Framework

To start using MediaPipe Framework, install MediaPipe Framework and start building example applications in C++, Android, and iOS.

MediaPipe Framework is the low-level component used to build efficient on-device machine learning pipelines, similar to the premade MediaPipe Solutions.

Before using MediaPipe Framework, familiarize yourself with the following key Framework concepts:

Community

Contributing

We welcome contributions. Please follow these guidelines.

We use GitHub issues for tracking requests and bugs. Please post questions to the MediaPipe Stack Overflow with a mediapipe tag.

Privacy Notice

Last modified: June 5, 2026

When you use MediaPipe Tasks, processing of the input data (e.g. images, video, text) takes place on device, and MediaPipe does not send that input data to Google servers. As a result, you can use our MediaPipe Tasks APIs for processing data that should not leave the device.

MediaPipe Tasks APIs send metrics about the performance and utilization of the APIs in your app to Google. Google uses this metrics data to measure performance, usage, debug, maintain and improve the MediaPipe Tasks, as further described in our Privacy Policy.

You are responsible for obtaining informed consent from your app users about Google's processing of MediaPipe metrics data as required by applicable law.

Resources

Publications

Videos

mediapipe (google-ai-edge/mediapipe)

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37.3K ·GitHub
Mediapipe
这是什么东西？Google 的开源跨平台工具包，用于在实时和流式媒体中做端侧机器学习：提供现成的库和预训练模型，可以直接接入你自己的应用。
它是一个库，还是一整套工具箱？是一整套工具箱。MediaPipe Solutions 把跨平台的 Task API 和库与预训练、开箱即用的模型打包在一起；而底层的 MediaPipe Framework 则让你构建自己的高效端侧机器学习流水线。
我该怎么上手？先选一份对应你要做的视觉、文本或音频任务的开发者指南，然后按照你的平台的安装指南操作：Android、Web 应用或 Python。如果想用底层的 Framework，安装后就可以开始构建 C++、Android 和 iOS 的示例应用。
我需要会写 C++ 才能用它吗？用现成的 Solutions 不需要——你通过跨平台的 MediaPipe Tasks API 来使用它们，并有 Android、Web 和 Python 的指南。只有当你要在底层 Framework 上构建自定义流水线时才需要 C++。
仓库里的所有内容目前还在积极维护吗？不是。旧的 MediaPipe Legacy Solutions 的支持已于 2023 年 3 月 1 日结束——其代码和预编译二进制文件仍按原样提供，正逐步被新的 MediaPipe Solutions 取代。
用它开发的应用能运行在哪里？移动端（Android 和 iOS）、Web、桌面、边缘设备和物联网——同一套方案可以接入所有这些开发平台上的应用。
我用户的数据会离开设备吗？不会——图像、视频、文本等输入数据都在设备上处理，不会发送到 Google 服务器。但有一点要注意：Tasks API 会向 Google 发送性能和使用情况指标，因此你可能需要为此征得用户同意。
我究竟能用它做出什么东西？项目的发布列表提到了实时手部追踪、基于 BlazePose 的全身姿态追踪、带深度估计的人脸和眼部追踪、3D 物体检测、即时运动追踪，以及用 AutoFlip 做智能视频重构——Google Meet 背景功能用的就是同一类技术。
我能用自己的数据调整这些模型吗？可以。MediaPipe Model Maker 能用你的数据定制方案模型，MediaPipe Studio 让你在浏览器里对模型进行可视化、评估和基准测试；而且这些方案本身是开源项目的一部分，你也可以直接修改方案代码。
在我自己的产品里使用它收费吗？免费——它是以 Apache-2.0 许可证开源的，设计初衷就是让你把这些方案接入覆盖移动端、Web 和桌面的应用。
What are similar projects?silero-vad, opencv, coyopedal, runanywhere-sdks, supertonic, ncnn, SmarterStreaming, MNN
Which topics is it in?stream-processing, on-device-ai, edge-ai, c-plus-plus, audio-processing, iot, mobile-development, video-processing, google, real-time, artificial-intelligence, streaming
Creator
google-ai-edge
Size
595.3 MB
Stars
37.3K
Updated
CommitOct 16Releasev1.1.1Oct 14
Age
7y
Commits · 90 Days