A week ago a dying worm slowly crawled into a small pocket of space. I was interested at the possibility of seeing what the decomposition process would look like. A few days in and this happens:
I didn't expect to see something so violent! The timelapse videos shows in 10 seconds what in reality took nearly 12 hours. Maybe the speed at which it plays back is what makes it feel so violent?
It would be great to develop a system that allows for real time viewing, but that will have to wait.
Not sure what type of slug was eaten, the predator in this case appears to be some sort of Beetle Larvae? You can also make out the worm in the first few seconds of video, the view of which is quickly obscured as the Beetle Larvae makes its way in.
A few days ago a couple small explosions of tiny white bugs appeared. Zooming in shows what appear to be hundreds of tiny Pillbugs (Armadillidium vulgare, roly-polies : )! Action begins approximately 9 seconds in.
According to this article these young crustaceans have already spent 10 to 14 weeks riding around in the pouch of a female Pillbug. I wonder if we could look back at previous video and identify any of them?
The area represented in the video is approximately ~8.1cm x 4.5cm (3.2" x 1.8"). This puts the young Pillbugs at ~1.5mm (1/16") in length.
Most of the Pillbugs quickly disperse beyond the viewable area within a few seconds. A few slow down and some can be seen feeding, or being fed on.
So much happening in such little space, so much more happening that's barely visible, and I can only begin to imagine how much is going on that isn't visible at all at this level.
Equivalent to a 35 megapixel camera. Pew pew megapixels! Unfortunately, the videos being posted are processed down to ~1920x1080.
It would be interesting to see the timelapse at 4k on an appropriate monitor, but it's more interesting to start looking closer. Since we're capturing the images at a higher res than being displayed, we can crop out a 1920x1080 section of the native res image and turn that into a video. Here's what that looks like over the course of a week:
The video above is of a ~3.2" x 1.8" splice of the earth. One week = 604800 seconds. This plays back in ~66 seconds. Life at ~9163x!
The scanner being used can capture images at 2400 DPI, and scanners that can capture at 9600 DPI aren't terribly expensive (though they do get bulkier). Capturing a full image at that res is probably too much for the Raspberry Pi to handle, and the storage space would be excrutiating.
But capturing even a square inch of space at that DPI would be fascinating, and I think doable : )
For now, I wonder what square inch would be most interesting to capture at 2400DPI?
Also, latest images are being posted at the "Latest Image" tab above. Approximately ~10 minute delay.
Ann Arbor saw between 1 and 3 inches of rain on Sunday, June 14th. It's interesting to see what the rain does to freshly (~two week) turned soil.
Playing around with imagemagick a bit more. I'm splicing a 100x100px section and getting the 'average brightness' of it. Scans are taken every 5 minutes, but to keep things simple I'm only sampling every third image, or roughly every 15 minutes.
Then I repeat that process 100px down, continuing until we get to the bottom of the image. Imagemagick spits out a pile of numbers like so:
Reading Date
Rainfall (in.)
0-100
100-200
200-300
300-400
400-500
500-600
600-700
700-800
800-900
0:00:00
0
9598.33
13316.9
10462.3
10667.8
10865.2
10513.7
10696.4
10353.2
10342.7
0:15:00
0
9460.71
13131.9
10450.9
10675.1
10881.2
10513.1
10743.1
10371.1
10354.6
0:30:00
0
9237.39
12896.3
10400.8
10698.7
10877.8
10542.1
10744.6
10392.1
10368.6
0:45:00
0
9155.23
12850.5
10315
10650.4
10851.8
10505.7
10729.3
10370.2
10382.1
1:00:00
0
9487.32
12982.9
10343.6
10668.1
10847
10512
10734.3
10374.6
10384
1:15:00
0
9519.43
12857.8
10288.2
10630.3
10810.6
10464.2
10685.2
10365.9
10339.7
1:30:00
0
9623.34
12937.3
10331.2
10609.4
10802.6
10485.5
10736.8
10393.2
10421.1
1:45:00
0
9516.39
12664.1
10087.5
10552.4
10765.2
10437.9
10715.7
10369.7
10345.9
2:00:00
0
9497.97
12638.1
10084.9
10480.2
10656.9
10417.9
10720.8
10366.6
10347.4
2:15:00
0
9586.58
12693.7
10088.4
10481.7
10597.6
10347.9
10712.9
10380.3
10333.1
2:30:00
0
9770.57
12844.1
9968.46
10395.3
10489.6
10198.7
10651.9
10324.4
10265.1
2:45:00
0
9660.3
12739.6
10046.8
10414.7
10511.4
10188
10619.8
10355.5
10314.7
3:00:00
0
9605.28
12698.8
10024.2
10384.2
10493.6
10182
10594
10318.9
10277.6
3:15:00
0
9569.22
12469.8
10023.2
10386.9
10474.1
10120.6
10551.8
10289.6
10249.6
3:30:00
0
9575.44
12534.9
10039.6
10409
10488.7
10132.1
10541.7
10279
10242.2
3:45:00
0
9509.7
12525.6
10005.7
10370.4
10443.8
10094.8
10502.8
10236.6
10190.7
4:00:00
0
9598.07
12539.1
9968.55
10371.2
10460.4
10099.1
10510.9
10232.1
10183.5
Reading Date and Rainfall (in.) are coming from elsewhere. The first 100px is mostly black, this part of the scanner is above ground and since it's night there is little for the scanner's light to reflect off. A graph of the 400-500px region from 00:00 to 4:00:00 looks like:
The numbers above unfortunately represent the day prior to the rainfall. Whoops. In the morning I'll have numbers for the proper day and be able to compare them with data from the City of Ann Arbor's Rain Gauges. Some time later this week I'll post the results.
I'm wondering if I can show to some degree of reliability how far and how quickly the rain is penetrating into the soil with this setup. I guess I should build/buy some soil sensors at this point to compare : )
Also, currently using Imagemagick's identify -format '%[mean]' command to infer "image brightness", but I'm really not sure what the command is doing / how it comes up with the numbers it spits out....
So much to learn!
Updated:
Comparison of rainfall vs. image brightness for June 14th. This is a 100px snapshot ~4 inches below the surface. Both the rainfall and image brightness values were remapped from 0 - 100.
I'm doing a couple things here that I'm pretty sure are bad ideas.
1. I'm analyzing the jpeg, not the original tiff file.
2. I'm analyzing a section of a copy of the original jpeg, more loss : )
3. I have no real clue what I'm doing with the math. I think I used the same method I used to remap and constrain light sensor values on an arduino project from years ago for this (return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min;)...
4. Wheeeee!
Imagemagick is a wonderful suite of tools that allow people to do lots of interesting things with images. Combining it with very minimal scripting knowledge lets one easily automate processing of large batches of images.
I used ImageMagick's compare tool to highlight in red the difference between 286 sequential images captured over 24 hours. I then used IM's convert tool to remove all other colors, create a transparent background, and finally stack each image onto the next.
Avconv was then used to turn these into a short 10 second video.
It's not terribly clear, but it does highlight the path(s) worms are taking. The large blob of red that shows up at the top is sunlight penetrating the first inch or two of topsoil/debris.
Very little attention to any sort of detail has been taken with this. This was a terribly fun distraction from documenting the actual setup - which I should really finish up in the next week or two : ) Regardless, I am constantly amazed at the amount of blood sweat and tears that are freely available in the software world.
I took a portable version of the scanning setup to the beaches of Lake Michigan last week. It was interesting, not as successful as I would have liked it to be but I did learn a bit, took some notes, and have a better idea of where things can be improved.
The home setup continued to work fine while I was gone. The biggest issue I have to resolve is storage, switching to a portable USB drive should resolve that, but I'll miss the joy of uploading everything to a remote server, crunching all the images into a video and uploading to youtube / wherever at high speed. It doesn't make sense at this point to justify spending $50 / month when a 1TB drive can be purchased for $70.
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Aside from technical fun, it looks like we've had a creature of some sort dig a hole in front of the scanner! No good images of the animal itself, the limited depth of field only gives us blurry images of anything more than 1 or 2 mm away from the scanner.
Lots of work still needed on the Pi setup process.
- Still trying to decide how much information to include here
- - Is it necessary to discuss initial image / OS installation, network setup,
- - This version assumes everything happens on the Pi - lots of pros & cons with this : )
- Still need to compile a section on device setup (hub/scanner/wifi) along with pros & cons of different device choices.
I would love to spend more time with someone discussing and either building or linking to information that helps myself and others understand what can be seen in the images.
- Root Growth
- Insect life / processes that we're able to see
- Soil type
- Seasonal differences
Last, another objective has been to get battery powered version running. I spent a couple hours today setting up a battery (on loan from the wonderful AADL). At roughly 500mA idle and 900mA scanning (figuring 1 min/scan) should provide plenty excess energy to document for a solid day. (27,000mAh Battery).
Though my current understanding is that I can not charge the battery AND power the setup at the same time. The specs for the battery specify a max of 1 Amp output. With a WiFi dongle power hitting 1007mAmps for roughly 30 seconds at a time. It was recommended I avoid exceed the max amperage, even if just a little.
