R image processing

[mfidino]

These PR is a quality of life improvement for R users of autofocus. The old example assumed that images have already been preprocessed and/or zipped together. This is not the case when someone has a whole bunch of camera trap images in hand.

This new example process_predict_example.R contains a suite of functions that can be used to:

1. Collect the file names of images that you want to process (via Dan Acheson)
2. Process the images in a way similar to process_raw.py. We remove the bottom 198 pixels and then reduce to 760x512 pixels.
3. Zip images together into 'bundles' of 10.
4. Post the zip files to autofocus, which goes much faster than posting single images.
5. Process the output from autofocus to generate the 'most likely' estimate in a photo (i.e., going from the many probability statements to the maximum probability statement).

In R, you then end up with a svelt data.frame that contains the original file name and what the most likely species in that photo is. As an example:

best_ids
# A tibble: 7 x 3
  file                                          species probability
  <chr>                                         <chr>         <dbl>
1 C:/Users/mfidino/Documents/GitHub/autofocus/~ squirr~       0.584
2 C:/Users/mfidino/Documents/GitHub/autofocus/~ bird          0.986
3 C:/Users/mfidino/Documents/GitHub/autofocus/~ raccoon       0.960
4 C:/Users/mfidino/Documents/GitHub/autofocus/~ rabbit        0.934
5 C:/Users/mfidino/Documents/GitHub/autofocus/~ raccoon       0.997
6 C:/Users/mfidino/Documents/GitHub/autofocus/~ skunk         0.999
7 C:/Users/mfidino/Documents/GitHub/autofocus/~ deer          1.000

Finally, the images (and associated zip files) that get processed are treated as temporary files so you don't have to have to create a secondary batch of images. This is most useful for the prediction side of autofocus (for training we'd probably want to retain the processed images).

[gsganden]

Got it, will review when I get a chance.