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• overall goal: subtract dark, denies, signal ROI, bunch split
• subtract dark image
• run denoting algorithm median filter
• median filter (for smoothing):
  • looks at pixel and its neighbors (number can be specified in some manner not yet understood)
  • take median of that set and set the center pixel value to the median
• look at noise region
• subtract mean of noise from whole image
• anything >10 (can be over-ridden) standard deviation of noise, keep it.  < 10 stddevs set it to zero.
• normalize image so sum=1
• assumes dark image is larger than the shot image ROI (xmin,xmax,ymin,ymax probably coming from EPICS)
• looks at max value in normalized image
• takes all pixels>0.2*max then you "stay" in the ROI.  the software "draws a rectangle" around these pixel to keep ROI rectangular
• expands rectangle dimensions by 2.5 (from the center) (user-settable) to bring in all interesting pixels into ROI, with a high likelihood
• calls splitimage (says 'not done' in code?) to handle the bunches
  • this calls IslandSplitting which calls scipy.measurement.label on a boolean image, where the threshold for computing the boolean is zero (this is a very simple not really Otsu's method with no automatic search for the threshold, we believe)
• splitimage returns a 3D array where first dimension is the bunch (i.e. a set of rectangular ROIs)

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