https://github.com/kootenpv/whereami

Uses WiFi signals :signal_strength: and machine learning to predict where you are
https://github.com/kootenpv/whereami

access-point cross-platform distance hacktoberfest hacktoberfest2021 indoor-positioning whereami wifi-signal

Last synced: 2 days ago
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Uses WiFi signals :signal_strength: and machine learning to predict where you are

• Host: GitHub
• URL: https://github.com/kootenpv/whereami
• Owner: kootenpv
• License: agpl-3.0
• Created: 2016-09-18T17:50:24.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2023-11-30T10:27:09.000Z (about 1 year ago)
• Last Synced: 2024-12-04T13:04:14.285Z (9 days ago)
• Topics: access-point, cross-platform, distance, hacktoberfest, hacktoberfest2021, indoor-positioning, whereami, wifi-signal
• Language: Python
• Homepage:
• Size: 64.5 KB
• Stars: 5,119
• Watchers: 100
• Forks: 246
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## whereami

[![Build Status](https://travis-ci.org/kootenpv/whereami.svg?branch=master)](https://travis-ci.org/kootenpv/whereami)

[![Coverage Status](https://coveralls.io/repos/github/kootenpv/whereami/badge.svg?branch=master)](https://coveralls.io/github/kootenpv/whereami?branch=master)

[![PyPI](https://img.shields.io/pypi/v/whereami.svg?style=flat-square)](https://pypi.python.org/pypi/whereami/)

[![PyPI](https://img.shields.io/pypi/pyversions/whereami.svg?style=flat-square)](https://pypi.python.org/pypi/whereami/)

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Your computer will known whether you are on Couch #1 or Couch #2.

## Cross-platform

Works on OSX, Windows, Linux (tested on Ubuntu/Arch Linux).

The package [access_points](https://github.com/kootenpv/access_points) was created in the process to allow scanning wifi in a cross platform manner. Using `access_points` at command-line will allow you to scan wifi yourself and get JSON output.

`whereami` builds on top of it.

### Installation

    pip install whereami

### Usage

```bash

# in your bedroom, takes a sample

whereami learn -l bedroom

# in your kitchen, takes a sample

whereami learn -l kitchen

# get a list of already learned locations

whereami locations

# cross-validated accuracy on historic data

whereami crossval

# 0.99319

# use in other applications, e.g. by piping the most likely answer:

whereami predict | say

# Computer Voice says: "bedroom"

# probabilities per class

whereami predict_proba

# {"bedroom": 0.99, "kitchen": 0.01}

```

If you want to delete some of the last lines, or the data in general, visit your `$USER/.whereami` folder.

### Python

Any of the functionality is available in python as well. Generally speaking, commands can be imported:

    from whereami import learn

    from whereami import get_pipeline

    from whereami import predict, predict_proba, crossval, locations

### Accuracy

k

Generally it should work really well. I've been able to learn using only 7 access points at home (test using `access_points -n`). At organizations you might see 70+.

Distance: anything around ~10 meters or more should get >99% accuracy.

If you're adventurous and you want to learn to distinguish between couch #1 and couch #2 (i.e. 2 meters apart), it is the most robust when you switch locations and train in turn. E.g. first in Spot A, then in Spot B then start again with A.

Doing this in spot A, then spot B and then immediately using "predict" will yield spot B as an answer usually. No worries, the effect of this temporal overfitting disappears over time. And, in fact, this is only a real concern for the very short distances. Just take a sample after some time in both locations and it should become very robust.

Height: Surprisingly, vertical difference in location is typically even more distinct than horizontal differences.

### Related Projects

- The [wherearehue](https://github.com/DeastinY/wherearehue) project can be used to toggle Hue light bulbs based on the learned locations.

###  Almost entirely "copied" from:

https://github.com/schollz/find

That project used to be in Python, but is now written in Go. `whereami` is in Python with lessons learned implemented.

### Tests

It's possible to locally run tests for python 2.7, 3.4 and 3.5 using tox.

    git clone https://github.com/kootenpv/whereami

    cd whereami

    python setup.py install

    tox