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https://github.com/felixdivo/hackathon-2024

ARKU + ProKI + KompAKI
https://github.com/felixdivo/hackathon-2024

hackathon manufacturing vision

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ARKU + ProKI + KompAKI

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README 

        
# Hackathon 2024



Welcome, and thank you for participating! This repository contains a skeleton for your submission to the Hackathon 2024.



## The Challenge



The challenge was presented during the kick-off meeting.

In short, the task is to find a good position and rotation for a robotic arm to grab a piece of cut sheet metal.



An example input image 

An example solution



### Where is my data?



First off: The data is NOT to be made public. In particular, do not publicly upload the data to GitHub.



You should have received a link to a shared folder with the data in the kick-off.

Please add the data from the BWSync into `data/raw/` and `data/evaluate/`.



For example, `data/raw/part_1` now contains `part_1.png` for the part file and `gripper_2.png` for the gripper mask.

Each parts folder may contain several parts and gripper files, so you need to be able to compute matches for all of them.

Additionally, `data/evaluate/` also contains solutions for the evaluation.



We will run the final evaluation on a *different* dataset of a similar format, so make sure your solution is general enough to work on unseen data.

We might assess the effctivenss of your method on more challanging data if that helps to more clearly determine winners.



A pixel corresponds to 1mm in real world. The gripper SVGs are scaled accordingly.



## Your solution and the evaluation



You can either:

1. Use Python 3.12+ and the provided code skeleton to implement your solution. To get started, place your dependencies in `requirements.txt` and start coding in `solution/`. Your dependencies should be easy to install via pip. If that is not the case, see option 2.

2. Use your own tools and languages. In this case, provide us with a detailed description of how to run your code AND a `Dockerfile` where everything can run out of the box.



### The interface to your Code



To check if your code is working and to permit final grading, please make sure you provide the following:

Your solution must be a program with two strings as the calling arguments.

Firstly, it receives a CSV file containing the part and gripper image file names.

We might split the evaluation into multiple such calls to your program.

All paths are relative to the current working directory.

The input file will look like this:



```csv

part,gripper

part_1.png,gripper_5.svg

part_42.png,gripper_1.svg

```



Secondly, it receives an argument with a path to a folder where the results should be saved.

The output must look like this:



```csv

part,gripper,x,y,angle

part_1.png,gripper_5.svg,100,200,45

part_42.png,gripper_1.svg,300,400,90

```



You are more than welcome to add visualization images as well.

They might help you and us to understand your solution better.

You can simply return them as an additional `visualization` column in the CSV file.



In summary, your program needs to handle (for instance, with Python) `python solution/main.py path/to/input/tasks.csv output/solutions.csv`.

it should exit with a status code of `0` if everything went well and `1` in any other case.

Hint: In Python, the return code is always `0` anyway unless you explicitly call `sys.exit(1)` or an exception is raised.



### Runtime requirements



Your code needs to be able to run on a modern consumer desktop.

It may use a single GPU you'd typically find in such a machine (i.e., no high-end server GPUs).

The code may run for up to 3 seconds per part-gripper pair.

If it takes noticeably longer, we will deduct points from your score.



### Evaluation of your Code



To see if your program is running correctly, we provide a simple evaluation script.

It should work as long as your output follows the format described above.

You can run it by the following command:



```bash

python evaluate/eval.py

```



If you build something custom, you can also run it like this:



```bash

python evaluate/eval.py 'python solution/your/super/fancy/tool.py'

# Or

python evaluate/eval.py 'super_fast_assembly_solution'

```



**NOTE: This is currently a placeholder evaluation script. We will provide a complete evaluation on the actual data soon. It will also account for possibly ambigous solutions.**



## License



All resources in this repository are licensed under the MIT License. See the [LICENSE](LICENSE) for more information.



We expect you to also license your code under the MIT License when submitting it.



## Acknowledgments



Logos



This project is partially funded by the German Federal Ministry of Education and Research (BMBF) within the “The Future of Value Creation – Research on Production, Services and Work” program (funding number 02L19C150) managed by the Project Management Agency Karlsruhe (PTKA).

The authors are responsible for the content of this publication.