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)

Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

RNN 01- RNN_Classification Simple RNN training for classification task of 3 signal: Sine, Square, Triangle. 02- RNN_Regression Simple RNN training for

Nahid Ebrahimian 13 Dec 13, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023
code for "Self-supervised edge features for improved Graph Neural Network training",

Self-supervised edge features for improved Graph Neural Network training Data availability: Here is a link to the raw data for the organoids dataset.

Neal Ravindra 23 Dec 02, 2022
This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection', CVPR 2019.

Code-and-Dataset-for-CapSal This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detec

lu zhang 48 Aug 19, 2022
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati

Computer Vision Insitute, SZU 113 Dec 27, 2022
Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface Usage TransformMultiStepScheduler import albumentations a

19 Aug 04, 2021
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Thalles Silva 1.7k Dec 28, 2022
Dahua Camera and Doorbell Home Assistant Integration

Home Assistant Dahua Integration The Dahua Home Assistant integration allows you to integrate your Dahua cameras and doorbells in Home Assistant. It's

Ronnie 216 Dec 26, 2022
Inferring Lexicographically-Ordered Rewards from Preferences

Inferring Lexicographically-Ordered Rewards from Preferences Code author: Alihan Hüyük ([e

Alihan Hüyük 1 Feb 13, 2022
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Facebook Research 75 Dec 19, 2022
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
Code for layerwise detection of linguistic anomaly paper (ACL 2021)

Layerwise Anomaly This repository contains the source code and data for our ACL 2021 paper: "How is BERT surprised? Layerwise detection of linguistic

6 Dec 07, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

ProGen - (wip) Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily

Phil Wang 71 Dec 01, 2022
Large-Scale Unsupervised Object Discovery

Large-Scale Unsupervised Object Discovery Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce [PDF] We propose a novel ranking-based

17 Sep 19, 2022
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud This repository contains a reference implementation of our Part-Aware Data Augment

Jaeseok Choi 62 Jan 03, 2023
A python library for self-supervised learning on images.

Lightly is a computer vision framework for self-supervised learning. We, at Lightly, are passionate engineers who want to make deep learning more effi

Lightly 2k Jan 08, 2023
Cross-Document Coreference Resolution

Cross-Document Coreference Resolution This repository contains code and models for end-to-end cross-document coreference resolution, as decribed in ou

Arie Cattan 29 Nov 28, 2022
Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021

DIFFNet This repo is for Self-Supervised Monocular DepthEstimation with Internal Feature Fusion(arXiv), BMVC2021 A new backbone for self-supervised de

Hang 94 Dec 25, 2022
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

Deepender Singla 1.4k Dec 22, 2022