Speedup Needed and Opportunities

[CameronBodine]

This issue will document workflows which would benefit from additional speed-up to lower overall processing time of sonar recordings.

• Mapping substrate probabilities/logits
• src correction: can this operation be vectorized?
• Speed up image warping

[CameronBodine]

SRC correction sped up using math.sqrt instead of np.sqrt. Also used int() instead of .astype(int). I see a speedup of 1.7x on the hello world scan when processing EGN and exporting WCR rect/non-rect imagery! Great work @dbuscombe-usgs! Still unsure how to vectorize (if it's even possible) but considering that task as complete for the time being. May return if struck with divine numpy inspiration in the future.

[CameronBodine]

This will also speed up the substrate mapping workflow, including rectifying probs/logits since each class needs to be slant range corrected. Checking that task as complete also. Leaving issue open to identify other sources for speedup.

[CameronBodine]

I found this beautiful solution to speeding up the warping process while rectifying sonar imagery:

scikit-image/scikit-image#6864 (comment)

For the example dataset using 7 threads for 7 images, the rectification process went from 23.8 seconds to 16.2 seconds! About to implement.