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)

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

Oleg Rybkin 36 Dec 22, 2022
LBK 35 Dec 26, 2022
Repository containing the PhD Thesis "Formal Verification of Deep Reinforcement Learning Agents"

Getting Started This repository contains the code used for the following publications: Probabilistic Guarantees for Safe Deep Reinforcement Learning (

Edoardo Bacci 5 Aug 31, 2022
Tooling for converting STAC metadata to ODC data model

手语识别 0、使用到的模型 (1). openpose,作者:CMU-Perceptual-Computing-Lab https://github.com/CMU-Perceptual-Computing-Lab/openpose (2). 图像分类classification,作者:Bubbl

Open Data Cube 65 Dec 20, 2022
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs at the moment, Cycles and Arnold supported

GafferHaven Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs are supported at the moment, in Cycles and Arnold lights.

Jakub Vondra 6 Jan 26, 2022
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks

MEAL-V2 This is the official pytorch implementation of our paper: "MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tric

Zhiqiang Shen 653 Dec 19, 2022
EM-POSE 3D Human Pose Estimation from Sparse Electromagnetic Trackers.

EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers This repository contains the code to our paper published at ICCV 2021. For ques

Facebook Research 62 Dec 14, 2022
A Python Package for Portfolio Optimization using the Critical Line Algorithm

PyCLA A Python Package for Portfolio Optimization using the Critical Line Algorithm Getting started To use PyCLA, clone the repo and install the requi

19 Oct 11, 2022
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
Gym for multi-agent reinforcement learning

PettingZoo is a Python library for conducting research in multi-agent reinforcement learning, akin to a multi-agent version of Gym. Our website, with

Farama Foundation 1.6k Jan 09, 2023
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022
[CVPR 2020] Transform and Tell: Entity-Aware News Image Captioning

Transform and Tell: Entity-Aware News Image Captioning This repository contains the code to reproduce the results in our CVPR 2020 paper Transform and

Alasdair Tran 85 Dec 13, 2022
CONditionals for Ordinal Regression and classification in PyTorch

CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About

7 Jul 25, 2022
The codes and related files to reproduce the results for Image Similarity Challenge Track 2.

ISC-Track2-Submission The codes and related files to reproduce the results for Image Similarity Challenge Track 2. Required dependencies To begin with

Wenhao Wang 89 Jan 02, 2023
Resources related to our paper "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain"

CLIN-X (CLIN-X-ES) & (CLIN-X-EN) This repository holds the companion code for the system reported in the paper: "CLIN-X: pre-trained language models a

Bosch Research 4 Dec 05, 2022
[제 13회 투빅스 컨퍼런스] OK Mugle! - 장르부터 멜로디까지, Content-based Music Recommendation

Ok Mugle! 🎵 장르부터 멜로디까지, Content-based Music Recommendation 'Ok Mugle!'은 제13회 투빅스 컨퍼런스(2022.01.15)에서 진행한 음악 추천 프로젝트입니다. Description 📖 본 프로젝트에서는 Kakao

SeongBeomLEE 5 Oct 09, 2022
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

DECA: Detailed Expression Capture and Animation (SIGGRAPH2021) input image, aligned reconstruction, animation with various poses & expressions This is

Yao Feng 1.5k Jan 02, 2023
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

12 Dec 26, 2022
Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

human-pose-estimation-3d-python-cpp RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used) 1. Run 1-1. RealSenseD435 (RGB) 480x640 + CPU

Katsuya Hyodo 8 Oct 03, 2022