##Abstract ###The Input Output Course (University of Applied Science, Potsdam) continued with the assignment to experiment with slime mold, physarum polycephalum to be exact, building a timelapse setup with a raspberry pi controlled camera and analysing the captured video with computer vision algorithm.
So, what's a slime mold again? It is an incredibly interesting being – the yellow complex with its tiny slimy roots lives, if not in a forest, in a petri dish and grows fairly quick once it is served something delicious as oat flakes for example. Its not a mushroom nor a plant. It has the capability to smell and locate energy resources and has a slime memory: as it grows, it leaves a slimy trail of dead cells behind.
##Timelapse Studio Setup In order to capture my own timelapse, a slimelapse actually, of the physarium growing, I attached my Nikon D90 DSLR to a RaspberryPi and attached both an old carton.
Unfortunately we already had bought a camera module for our RasperryPis, just to learn that those cheap cams had no refocusing and were impractical for this project. I decided to use a DSLR controlled by the RaspberryPi instead, which I described in detail.
With this setup I could capture about 700 medium sized images with a full battery load. In total I took 8,382 images during this experimental project.
##Experiment Then we had to come up with a suitable experiment of the physarum. After I experimented a lot with salt as a growth inhibitor, but my results weren't satisfying at all. Therefore I decided to analyse the slime molds behavior within a galton board like oat flake setup. I was curious how the mold would grow from flake to flake.
##Computer Vision In the end, we learned about computer vision (CV) algorithm and how to use them with processing. I wanted to create an algorithm that tracks the physarum growth and the order in which the oat flakes are eaten.
I realized the Physarum Tracking by isolating the saturation channel and tracking changing contours. The Oat Recognition on the other hand solely uses the first frame of the timelapse material and utilizes the lightness channel to track the right contours.
Once those two procedures intersect another oat has been captured by the physarum.
##What you'll need
- Processing 2.2.1
- Python Mode for Processing by Johnatan Feinberg
- OpenCV for Processing by Greg Borenstein
To run the Slime Tracker, copy the repos processing sketch. Be sure to switch to python mode once installed. The OpenCV library for processing is required.
##What's it for? I consider this project to be highly experimental and I don't expect any outcome in a higher sense. This tiny experiment taught me vast knowledge about raspberryPi and computer vision.
However, I'd love to see the galton board like physarum experiment to turn into a live online bitcoin gambling experience. Someday.
##Thanks I want to thank our tutor pal @fabiantheblind, whose inspiring excitement always pushed us further. Also, I have to thank my fellow students for their helping critisim and ideas.
##Ahoi Follow me on twitter @topada, or get in touch via mail info@topada.de, I'll keep you informed as soon as topada.de eventually rises from the grave.
##License
This project is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
###The source code is licensed under the MIT License (MIT)
Copyright (c) 2015 Jonas Köpfer
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