An open source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+. Including offline map and navigation.

Overview

title

Pi Zero Bikecomputer

An open-source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+

https://github.com/hishizuka/pizero_bikecomputer

News

  • 2021/4/18 Please reinstall pyqtgraph when using python3-pyqt5 in Raspberry Pi OS (skip check if using).
  • 2021/4/3 Please reinstall openant and pyqtgraph because both libraries are re-forked.
$ sudo pip3 uninstall pyqtgraph
$ sudo pip3 install git+https://github.com/hishizuka/pyqtgraph.git
$ sudo pip3 uninstall openant
$ sudo pip3 install git+https://github.com/hishizuka/openant.git

Table of Contents

Abstract

Pi Zero Bikecomputer is a GPS and ANT+ bike computer based on Raspberry Pi Zero(W, WH). This is the first DIY project in the world integrated with necesarry hardwares and softwares for modern bike computer. It measures and records position(GPS), ANT+ sensor(speed/cadence/power) and I2C sensor(pressure/temperature/accelerometer, etc). It also displays these values, even maps and courses in real-time. In addition, it write out log into .fit format file.

In this project, Pi Zero Bikecomputer got basic functions needed for bike computers. Next target is to add new functions which existing products do not have!

You will enjoy both cycling and the maker movement with Pi Zero Bikecomputer!

Here is detail articles in Japanese.

Daily update at twitter (@pi0bikecomputer), and my cycling activity at STRAVA.

system-01-202106

system-02

Features

  • Easy to make

    • Use modules available at famous Maker stores.
    • Assemble in Raspberry Pi ecosystems.
    • Install with basic commands such as apt-get install, pip and git command.
  • Customization

    • Need only modules you want to use. Pi Zero Bikecomputer detects your modules.
  • Easy to develop

    • Pi Zero Bikecomputer uses same libraries as for standard Linux.
    • So, you can run in cross platform environments such as Raspberry Pi OS, some Linux, macOS and Windows.
  • Good balance between battery life and performance

Specs

Some functions depend on your parts.

General

Specs Detail Note
Logging Yes See as below
Sensors Yes See as below
Positioning Yes A GPS module is required. See as below.
GUI Yes See as below
Wifi Yes Built-in wifi
Battery life(Reference) 18h with 3100mAh mobile battery(Garmin Charge Power Pack) and MIP Reflective color LCD.

Logging

Specs Detail Note
Stopwatch Yes Timer, Lap, Lap timer
Lap Yes [Total, Lap ave, Pre lap ave] x [HR, Speed, Cadence, Power]
Cumulative value Yes [Total, Lap, Pre lap] x [Distance, Works, Ascent, Descent]
Elapsed time Yes Elapsed time, average speed(=distance/elapsed time), gained time from average speed 15km/h(for brevet)
Auto stop Yes Automatic stop at speeds below 4km/h(configurable), or in the state of the acceleration sensor when calculating the speed by GPS alone
Recording insterval 1s Smart recording is not supported.
Resume Yes
Output .fit log file Yes
Upload to STRAVA Yes
Live sending Yes But I can't find a good dashboard service like as Garmin LiveTrack

Sensors

USB dongle is required if using ANT+ sensors.

Specs Detail Note
ANT+ heartrate sensor Yes
ANT+ speed sensor Yes
ANT+ cadence sensor Yes
ANT+ speed&cadence sensor Yes
ANT+ powermeter Yes Calibration is not supported.
ANT+ LIGHT Yes Bontrager Flare RT only.
ANT+ Control Yes Garmin Edge Remote only.
Bluetooth sensors No
Barometric altimeter Yes An I2c sensor(pressure, temperature) is required.
Accelerometer Yes An I2c sensor is required.
Magnetometer Yes An I2c sensor is required.
Light sensor Yes An I2c sensor is required. For auto backlight and lighting.

Positioning

Specs Detail Note
Map Yes Support map tile format like OSM. So, offline map is available with local caches.
Course on the map Yes A course file(.tcx) is required.
Course profile Yes A course file(.tcx) is required.
Cuesheet Yes Use course points included in course files.
Search Route Yes Google Directions API
  • Map with Toner Map
    • Very useful with 2 colors displays (black and white).
  • Map with custimized Mapbox
    • Use 8 colors suitable for MIP Reflective color LCD.
  • Course profile

GUI

Specs Detail Note
Basic page(values only) Yes
Graph Yes Altitude and performance(HR, PWR)
Customize data pages Yes With layout.yaml
ANT+ pairing Yes
Adjust wheel size Yes Set once, store values
Adjust altitude Yes Auto adjustments can be made only once, if on the course.
Language localization Yes Font and translation file of items are required.
No GUI option Yes headless mode
  • Performance graph
  • Language localization(Japanese)

Experimental functions

ANT+ multiscan

it displays three of the people around you in the order in which you caught sensors using ANT+ continuous scanning mode.

Comparison with other bike computers

  • 200km ride with Garmin Edge 830 and Pizero Bikecomputer (strava activity)

  • title-03.png

Items Edge830 Pi Zero Bikecomputer
Distance 193.8 km 194.3 km
Work 3,896 kJ 3,929 kJ
Moving time 9:12 9:04
Total Ascent 2,496 m 2,569 m

Hardware Installation

See hardware_installation.md.

Software Installation

See software_installation.md.

Q&A

License

This repository is available under the GNU General Public License v3.0

Author

hishizuka (@pi0bikecomputer at twitter, pizero bikecomputer at STRAVA)

This is the official implementation of Elaborative Rehearsal for Zero-shot Action Recognition (ICCV2021)

Elaborative Rehearsal for Zero-shot Action Recognition This is an official implementation of: Shizhe Chen and Dong Huang, Elaborative Rehearsal for Ze

DeLightCMU 26 Sep 24, 2022
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
Uses OpenCV and Python Code to detect a face on the screen

Simple-Face-Detection This code uses OpenCV and Python Code to detect a face on the screen. This serves as an example program. Important prerequisites

Denis Woolley (CreepyD) 1 Feb 12, 2022
Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression

Regression Transformer Codebase to experiment with a hybrid Transformer that combines conditional sequence generation with regression . Development se

International Business Machines 27 Jan 05, 2023
A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:

Squirrel Core Share, load, and transform data in a collaborative, flexible, and efficient way What is Squirrel? Squirrel is a Python library that enab

Merantix Momentum 249 Dec 07, 2022
Hypercomplex Neural Networks with PyTorch

HyperNets Hypercomplex Neural Networks with PyTorch: this repository would be a container for hypercomplex neural network modules to facilitate resear

Eleonora Grassucci 21 Dec 27, 2022
Code for the CVPR2021 workshop paper "Noise Conditional Flow Model for Learning the Super-Resolution Space"

NCSR: Noise Conditional Flow Model for Learning the Super-Resolution Space Official NCSR training PyTorch Code for the CVPR2021 workshop paper "Noise

57 Oct 03, 2022
EfficientMPC - Efficient Model Predictive Control Implementation

efficientMPC Efficient Model Predictive Control Implementation The original algo

Vin 8 Dec 04, 2022
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2020

Phillip Lippe 1.1k Jan 07, 2023
Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks.

Luminous is a framework for testing the performance of Embodied AI (EAI) models in indoor tasks. Generally, we intergrete different kind of functional

28 Jan 08, 2023
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb

Yu Tian 117 Jan 03, 2023
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A

1 Feb 10, 2022
noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

amos_xwang 57 Dec 04, 2022
Composable transformations of Python+NumPy programsComposable transformations of Python+NumPy programs

Chex Chex is a library of utilities for helping to write reliable JAX code. This includes utils to help: Instrument your code (e.g. assertions) Debug

DeepMind 506 Jan 08, 2023
Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.

.NET Interactive Notebooks for C# Welcome to the home of .NET interactive notebooks for C#! How to Install Download the .NET Coding Pack for VS Code f

.NET Platform 425 Dec 25, 2022
Setup freqtrade/freqUI on Heroku

UNMAINTAINED - REPO MOVED TO https://github.com/p-zombie/freqtrade Creating the app git clone https://github.com/joaorafaelm/freqtrade.git && cd freqt

João 51 Aug 29, 2022
Differentiable Simulation of Soft Multi-body Systems

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

YilingQiao 26 Dec 23, 2022
Global-Local Attention for Emotion Recognition

Global-Local Attention for Emotion Recognition Requirements Python 3 Install tensorflow (or tensorflow-gpu) = 2.0.0 Install some other packages pip i

Minh Nhat Le 15 Apr 21, 2022
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels (BMVC 2021)

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi Code

Subhabrata Choudhury 18 Dec 27, 2022
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au

14 Nov 28, 2022