https://github.com/dbpprt/deepoctoprint
Crowdsourced dataset collector for failing 3D printers 🏗️💥 (as of now)
https://github.com/dbpprt/deepoctoprint
deep-learning octoprint octoprint-
Last synced: 3 months ago
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Crowdsourced dataset collector for failing 3D printers 🏗️💥 (as of now)
- Host: GitHub
- URL: https://github.com/dbpprt/deepoctoprint
- Owner: dbpprt
- License: mit
- Created: 2020-05-26T19:38:11.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-12T21:58:49.000Z (over 3 years ago)
- Last Synced: 2025-06-06T12:48:36.521Z (12 months ago)
- Topics: deep-learning, octoprint, octoprint-
- Language: Python
- Homepage:
- Size: 341 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🤖 DeepOctoPrint 🤖 (*WIP: data collector only*)
  
([source](https://www.pinterest.ca/pin/287174913714717323/))
---
## TLDR 🦥
You own a 3D Printer? You're using OctoPrint? Your printer is equipped with a camera? You want to help in develop a deep nerual network to detect failing 3D prints without looking at your printer all the time? You're willing to donate frames of your camera to a public dataset?
Then you're the **right** person. Please install the plugin and help us to make a better world! (just kidding, I'm just tired of failing prints in an overnight print or when I'm at work).
You can download the plugin [here](https://addlinkhere) (*I'm sorry - you can't as of now* 😥)
## ~~TLDR~~ Slightly longer version 📚
Are you tired of failed prints during the night? Tired of clogging, spaghetti, warping and objects not sticking to the bed?
> ### ***I am!***
I started to look for object vision based solutions to monitor my prints or open datasets to train my own model without any success. There are several projects available like [The Spaghetti Detective](https://www.thespaghettidetective.com/), [Kronos Data Collector](https://github.com/MrBreadWater/project-kronos-data-collector), etc. but I was searching for a more open and extensible approach to solve this problem. But unfortunately everything starts with a **good** dataset 😣. I labeled a few hundred pictures of my beloved Prusa MK3S and trained several models to detect certain edge cases which worked out quite well but I want to find a generalized solution which can be used accross the 3D-Printing community. So let me just start with some core **tenets** of this project:
- Everything will be **open-source**!
- The dataset is crowdsourced and licensed as [**ODbL**](https://opendatacommons.org/licenses/odbl/1-0/index.html)
- Labeling of the data will be crowssourced, too.
- I'm very open for **contributors**, feel free to reach out to me, create a PR, create an issue, whatever necessary to make this project successful!
***Okay, but what is this project actually doing?***
Currently there is nothing more then a data collector, implemented as **OctoPrint** plugin, which takes a picture every **x**-seconds and sends it to a remote API. The code itself is pretty messy and subject to be improved (*feel free to leave a PR!*).
***TLDR:*** *Where can I download the plugin?*
**WIP**: The plugin is not yet published, I'm working on it...
## Table of contents 📋
* [Table of contents](#table-of-contents)
* [FAQ](#faq)
* [How to get started with the code](#how-to-get-started-with-the-code)
* [Roadmap](#roadmap)
* [Contributors](#Contributors)
## FAQ ⁉️
> **Where will my data be stored?**
The collected data will be stored in Frankfurt (DigitalOcean)
> **What happens if I'm accidentially naked in front of my 3D printer?**
The collected images will be *cleaned* in a unsupervised fashion. I'm using a simple model to detect human-beings and NSFW content within the pictures (and delete them...)
> **But why can't I see any machine learning models in this repository? 😱**
For now only the data collector plugin is available because as you know - every good model starts with a good dataset 😝
> **Hmm, when I install the data collector, what will happen with the pictures?**
By installing and enabling the plugin, you agree to publish the images to the public licensed as [**ODbL**](https://opendatacommons.org/licenses/odbl/1-0/index.html). The data collection is anonymously and the data will be kept indefinitely.
> **Where can I find the pictures in this repository which I published yesterday?**
The dataset will be cleaned and distributed at least once a month.
> **I want my data to be deleted because I printed** ***xxx*** **which I don't like to be part of a public dataset?**
The plugin writes a unique hex to your octoprint.yaml configuration file. Feel free to create an issue as fast as possible (before I publish the new version of the dataset) to get your data cleaned.
> **Why is the plugin licensed as GNU AFFERO GENERAL PUBLIC LICENSE Version 3?**
Because I borrowed some code from the Spaghetti Detective plugin (nice work btw!)
> **Why has this README so many spelling and grammar issues?**
Sorry, it is late and I'm not a native speaker - feel free to create a PR (*please*)...
> **How can I be added in the list of contributors?**
You're contributing to the dataset by using the collector plugin or contributed to the code? Just create a PR and add your name. Wait what - but how are you checking that people are really using the plugin? *I don't and I don't intend to do so!*
> **Will this be a cloud service or what are your plans?**
I intend to create a lightweight model which can be used on Jetson or similar. However at the beginning I will probably experiment with deeper models to find a good baseline.
> **How do you want to make this possible, do you actually have any idea of deep learning?**
Yes I have but this is not subject of this repository. I already explored several approaches using pixel-wise image segmentation to detect the print, the bed and the print head. This helps to detect separations from the print bed, *extereme* clogging and gives us a perfectly trained feature extractor backbone which can be used to explore certain other problems like spaghetti (Which can be easily part of the segmentation itself), warping, etc. I ended up exploring a multi-task learning approach with different losses accross the tasks and different weights for each loss. However this approach was highly complicated but I think it is feasible to use a big network to train a lightweight network in a semi-supervised approach to run it *at the edge*.
> **What framework do you prefer?**
Definitely **PyTorch!** 😍
> **Can I contribute to the project?**
Yes definitely (and I would highly appreciate it)! There are so many topics to work on, just to name a few:
- Improve the code - it has no tests, it's pretty hacky, not documented and maybe not using the right architecture.
- Correct my spelling mistakes. 🙃
- Automate some things (GitHub Actions, Dependabot, ...)
- Make all your friends to install the **collector plugin**
- ... and many more ...
> **What exactly will be tracked?**
- A unique identifier (hex format) which gets generated when you install the plugin the first time and gets stored in the OctoPrint configuration file
- A frame of your webcam
- The time when the print starts
**IMPORTANT:** the plugin only collects data when a print is running!
> **Why is there a MIT license in the project but you mentioned AGPLv3?**
Some parts of the repository are licensed under different licenses and contain separate LICENSE files.
- Data Collector Plugin (AGPLv3) - borrows code from [The Spaghetti Detective](http://thespaghettidetective.com/) (which is a nice project, take a look. The authors also share their public dataset.)
- The dataset itself (**not yet released**) is licensed as [**ODbL**](https://opendatacommons.org/licenses/odbl/1-0/index.html)
## How to get started with the code 🛠️
*to be done, I'm sorry*
## Roadmap 🏄
- [x] Publish the project on GitHub
- [ ] Publish the API
- [ ] Package the plugin and publish it as **OctoPrint** plugin
- [ ] Get this repository somehow spreaded into the world (*This checkbox will never be checked*)
- [ ] Find more contributors (*Same applies here*)
- [ ] Publish the first dataset
- [ ] Improve the Roadmap...
## Contributors ❤️
### Active (code / project contributors)
- [Dennis](https://github.com/dennisbappert)
- 😲 [your name can be here!](https://github.com/dennisbappert/deepoctoprint/pulls)
- 💤 it is quite empty here 💤
### Passive (dataset contributors)
- [Dennis](https://github.com/dennisbappert)
- 😲 [your name can be here!](https://github.com/dennisbappert/deepoctoprint/pulls)
- 💤 it is quite empty here 💤
## Bonus content 🙈
If you really read the entire page till here (and I admit it is pretty long and boring) it must be at least an interesting project so feel free to drop a 🌟 star or follow the project!
(PS: I just like **GIFS** and it is hopyfully eye-catching enough to make you read the above line)