Bee Motion I

I swarm of bees decided to honor my home with a surprise move-in.  Before I had them removed (gently and professionally), I grabbed some video (about an hour total) of their flying motion.

I can then compute the differences between images and then sum those differences into a 'long exposure'.  If I set a threshold correctly in my code, the motion of the bees just pops out.

Bees move very fast so this is a short 2 second exposure running at 30 Hz.  So this is the sum of a the differences between 60 contiguous video frames, thresholded so only the largest motions (the bees) shows up:

They are true artists and flying machines, or they really have no idea what they're doing.

Here's another 2 second sample:

Deconstructing Images III

Was going through some old photos, and came across this.  Dawn 10 Feb 2018:

I was impressed not only by the colors, but also the textures.

Anyhow, here's a combination of 'color-magnitude' diagrams put together in an artsy way, but using the colors you see in the image above.  In all my work, the data will always dictate.

The structure on the furthest left is B (x-axis, 0 to 255 left to right) vs R-G (y-axis, 0 to 255, bottom to top).  The next left structure (sort of attached to the first structure) is G vs R-B.  The structure bottom right is R vs G-B.

Deconstructing Images II

 Using my previous post's first image of the prickly pear cactus flowers, I'm now generating these "color-magnitude diagrams" using the color camera RGB values -- effectively a three-channel imaging spectrometer.

These are (from left to right): red versus green minus blue, green versus red minus blue, and blue versus green minus red.  Color value goes from left to right, 0 to 255.  Difference value goes from bottom to top, -255 to +255.  Most of the color differences are positive in this particular case.

These images are different that the previous because the pixels in these images have been weighted by the number of pixels corresponding to a particular color and color difference.

Deconstructing Images

 I start with a nice input image of some flowers on my prickly pear cactus:

I then generate a "color-magnitude" diagram for some configurations:

The left two panels show R on the x-axis, and G-B on the y-axis.  The white line separating the two panels vertically represents a value of zero.  So pixels appearing above the white line have a positive G-B value, and those below the white line have a negative G-B value.  The middle two panels show G versus R-B.  The right panels show B versus R-G.  The colors of each pixel correspond to their colors in the input image.  The x-axis range is 0 to +255 (left to right).  The y-axis range is -255 to +255 (bottom to top).

Here's another example, using the image of the Earth taken by Artemis II:

Here's R vs G-B, G vs R-B, and B vs R-G

clearly picks out landmasses, ocean, clouds, and aurorae!

Sandhill Cranes Long Exposure

30 second long exposure of a flock of Sandhill cranes 'circling' over my location a couple days ago.  The cranes are here from mid-October to about March every year.  I'm situated right over their daily flight path as they visit various playas and other locations with standing water this time of year

I See The Murmurations

I see the murmurations of the black-birds

And stand there in awe of the numbers

I think about the effort it took

To get them all there

- CosmicLettuce

(text generated by me, image generated from Grok)

Magnetometer Bike Ride

A bike ride with the magnetometer on my phone recording at about 60Hz.  I've got x, y, z, and r axis data:

And here are the corresponding power spectra (spectrograms):