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)

Implementation of BI-RADS-BERT & The Advantages of Section Tokenization.

BI-RADS BERT Implementation of BI-RADS-BERT & The Advantages of Section Tokenization. This implementation could be used on other radiology in house co

1 May 17, 2022
CS583: Deep Learning

CS583: Deep Learning

Shusen Wang 2.6k Dec 30, 2022
Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

PatchGraph This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Installation Creat

Paloma Sodhi 11 Dec 15, 2022
Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

Jhacson Meza 47 Nov 18, 2022
[ICSE2020] MemLock: Memory Usage Guided Fuzzing

MemLock: Memory Usage Guided Fuzzing This repository provides the tool and the evaluation subjects for the paper "MemLock: Memory Usage Guided Fuzzing

Cheng Wen 54 Jan 07, 2023
Examples of using f2py to get high-speed Fortran integrated with Python easily

f2py Examples Simple examples of using f2py to get high-speed Fortran integrated with Python easily. These examples are also useful to troubleshoot pr

Michael 35 Aug 21, 2022
Codes of the paper Deformable Butterfly: A Highly Structured and Sparse Linear Transform.

Deformable Butterfly: A Highly Structured and Sparse Linear Transform DeBut Advantages DeBut generalizes the square power of two butterfly factor matr

Rui LIN 8 Jun 10, 2022
PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper.

deep-linear-shapes PyTorch implementation of "Representing Shape Collections with Alignment-Aware Linear Models" paper. If you find this code useful i

Romain Loiseau 27 Sep 24, 2022
This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

🌈 ERASOR (RA-L'21 with ICRA Option) Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point C

Hyungtae Lim 225 Dec 29, 2022
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

M2m: Imbalanced Classification via Major-to-minor Translation This repository contains code for the paper "M2m: Imbalanced Classification via Major-to

79 Oct 13, 2022
PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

Duo Li 273 Dec 18, 2022
DenseNet Implementation in Keras with ImageNet Pretrained Models

DenseNet-Keras with ImageNet Pretrained Models This is an Keras implementation of DenseNet with ImageNet pretrained weights. The weights are converted

Felix Yu 568 Oct 31, 2022
source code of Adversarial Feedback Loop Paper

Adversarial Feedback Loop [ArXiv] [project page] Official repository of Adversarial Feedback Loop paper Firas Shama, Roey Mechrez, Alon Shoshan, Lihi

17 Jul 20, 2022
Tree LSTM implementation in PyTorch

Tree-Structured Long Short-Term Memory Networks This is a PyTorch implementation of Tree-LSTM as described in the paper Improved Semantic Representati

Riddhiman Dasgupta 529 Dec 10, 2022
Pixel-wise segmentation on VOC2012 dataset using pytorch.

PiWiSe Pixel-wise segmentation on the VOC2012 dataset using pytorch. FCN SegNet PSPNet UNet RefineNet For a more complete implementation of segmentati

Bodo Kaiser 378 Dec 30, 2022
Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".

Interactive Machine Comprehension with Dynamic Knowledge Graphs Implementation for the EMNLP 2021 paper. Dependencies apt-get -y update apt-get instal

Xingdi (Eric) Yuan 19 Aug 23, 2022
Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds

CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds Introduction This is the official PyTorch implementation of o

Yijia Weng 96 Dec 07, 2022
Implementation of "Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis"

Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis Abstract: This work targets at using a general deep lea

163 Dec 14, 2022
Inference pipeline for our participation in the FeTA challenge 2021.

feta-inference Inference pipeline for our participation in the FeTA challenge 2021. Team name: TRABIT Installation Download the two folders in https:/

Lucas Fidon 2 Apr 13, 2022