I paired an AMG8833 thermal sensor with my Adafruit Memento camera to build a thermal camera. I expected it to be an instructional learning project, I just didn't expect it to be a learning project about CircuitPython performance. First step was to add performance timers to quantify impact of future enhancements, which gave me a baseline. Here's an excerpt reflecting four frames rendered using TileGrid:

read 38087 scaled 3099 mapped 1789 grid 1728 blit 28223 refresh 360370 total 433296
read 37789 scaled 3099 mapped 1759 grid 1758 blit 30190 refresh 359803 total 434398
read 38713 scaled 3129 mapped 1788 grid 1729 blit 29683 refresh 362098 total 437140
read 38296 scaled 3129 mapped 1758 grid 1759 blit 29146 refresh 360579 total 434667

Total time per frame of roughly 430ms means a little over 2 frames per second.

Back to Bitmap

I converted the code back to my naive dot-drawing code, which showed better numbers. Again, an excerpt of four frames:

read 37760 scaled 3368 mapped 1609 blit 27239 grid 146836 refresh 24468 total 241280
read 38266 scaled 3099 mapped 1580 blit 27239 grid 118077 refresh 24557 total 212818
read 38206 scaled 3368 mapped 1609 blit 27746 grid 144750 refresh 24527 total 240206
read 38237 scaled 3367 mapped 1610 blit 27269 grid 144750 refresh 24378 total 239611

My dot-drawing code is within the "grid" bracket and that's why it got a lot slower. And "refresh" is technically wrong as I'm no longer calling display.refresh(). I'm actually calling pycam.blit() but since I'm already using the "blit" label for something else I left the label as "refresh".

At a total cycle time of under 240ms, this was about 4 fps and almost double the speed of my TileGrid version. This is still very slow but the good news is the slowest parts are now code under my control.

Add Interpolation

With code under my control, NumPy experiment begins. I started by adapted PyGamer Thermal Camera code to my project. It replaced my old code within "scaled" and output a 15x15 array of interpolated values. Despite this added functionality, execution time dropped from ~3.3ms to ~0.6ms. Nice!

Unfortunately overall frame rate dropped from ~4fps to ~3fps because "grid" got slower: it now has to draw a thermal overlay of 15x15 data points instead of just 8x8.

read 38028 scaled 596 mapped 1520 blit 27626 grid 224501 refresh 24528 total 316799
read 38237 scaled 596 mapped 1520 blit 28789 grid 223636 refresh 24438 total 317216
read 38296 scaled 566 mapped 1580 blit 27567 grid 226170 refresh 24438 total 318617
read 38356 scaled 626 mapped 1728 blit 28849 grid 198901 refresh 24587 total 293047

Slower frame rate is only a temporary setback, because this example helped me learn how (the ulab.numpy subset of) NumPy can be applied to my project. These lessons helped me unlock additional performance gains.