GNU Affero General Public License v3.0

项目介绍

schollz/find 是一个托管在 GitHub 上的开源项目,主要使用 Go 编写,采用 GNU Affero General Public License v3.0 许可证,目前获得 5.1k 个 Star、365 次 Fork。

仓库全名
schollz/find
开源许可证
GNU Affero General Public License v3.0
Star 数
5.1k
Fork 数
365
未关闭 Issue
85

README 摘要

Keywords: indoor GPS, WiFi positioning, indoor mapping, indoor navigation, indoor positioning There is a new version, FIND3. It has numerous improvements over this version. About The Framework for Internal Navigation and Discovery (FIND) allows you to use your (Android) smartphone or WiFi-enabled computer (laptop or Raspberry Pi or etc.) to determine your position within your home or office. You can easily use this…

完整内容见 GitHub 原始 README 或本页 。

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find 用什么语言开发?

主要使用 Go 编写,采用 GNU Affero General Public License v3.0 许可证,目前有 5.1k 个 Star。

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仓库标注的许可证为 GNU Affero General Public License v3.0,商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 find 的源码?

可以直接克隆仓库:git clone https://github.com/schollz/find.git,默认分支为 master;安装与运行步骤见仓库 README。

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仓库最近一次更新为 2022年9月7日,当前有 85 个未关闭 Issue;Star 数为 5.1k,可作为社区活跃度的参考。

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Release

README

FIND

Version 2.4 Github All Releases FIND documentation Coverage Donate Say Thanks

Keywords: indoor GPS, WiFi positioning, indoor mapping, indoor navigation, indoor positioning

There is a new version, FIND3. It has numerous improvements over this version.

About

The Framework for Internal Navigation and Discovery (FIND) allows you to use your (Android) smartphone or WiFi-enabled computer (laptop or Raspberry Pi or etc.) to determine your position within your home or office. You can easily use this system in place of motion sensors as its resolution will allow your phone to distinguish whether you are in the living room, the kitchen or the bedroom, etc. The position information can then be used in a variety of ways including home automation, way-finding, or tracking!

Simply put, FIND will allow you to replace tons of motion sensors with a single smartphone!

The system is built on two main components - a server and a fingerprinting device. The fingerprinting device (computer program or android app) sends the specified data to the machine learning server which stores the fingerprints and analyzes them. It then returns the result to the device and stores the result on the server for accessing via a web browser or triggering via hooks.

FAQ (abbreviated):

More questions? See the unabbreviated FAQ.

Quickstart

If you’d like to install things yourself, see the documentation. You don’t need to do this to try it though. Follow the 3 steps below to get started quickly.

1. Download the software

Android users: download the current version of the app. Sorry iPhone users but the Apple store prevents apps that access WiFi information, so I will be unable to release a iPhone version.

OR

Computer users: you can download the current version of the fingerprinting program, available for Rasbperry Pi, OSX, Linux, and Windows.

2. Gather fingerprint data

First, to get started using FIND you will need to gather fingerprint data in your locations.

Android users: When you start up the app you will be asked for a username (enter whatever you want) and you’ll be assigned a unique group name. Simply click “Learn” and you’ll be prompted for a location name. After you enter a location, the app will connect to the server and then submit fingerprints.


Computer users: To start learning locations simply use ./fingerprint -e.

3. Track yourself

Once you’ve collected data in a few locations, you can track yourself.

Android users: Just press the “Track” button when you’re ready to track.

Computer users: Type in ./fingerprint to start tracking yourself.

More information

See the documentation at https://www.internalpositioning.com.

Acknowledgements

Funding from Duke University Colab

Thanks to tscholl2, sjsafranek, and jschools for their help in guiding the development of FIND and creating the early versions of FIND with me! Thanks to Rishabh Rajgarhia and CanvasJS for help implementing a nice graph. Thanks arafsheikh for adding interface selection, Pugio and ScottSWu for adding OS X/Windows support for the fingerprint program, including a better Windows scanning utility! Thanks Thom-x for the Dockerfile. Thanks certifiedloud for implementing the change to DELETE requests and implementing sockets for unix. Thanks bebus77 for making a awesome generic struct for OS support on the fingerprinting program! Thanks christoph-wagner for help with polling interval on app. Thanks to patorjk and asciiworld for the ASCII art. Thanks to Imgur for hosting images.

Like this? Help me keep it alive by donating $5 to pay for server costs.

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