Eduardo Ismael wrote:
im = Image.open("C:\Scan_119.jpg")
pixels = im.load()
width, height = im.size
def distance (a, b):
return ((a[0]- b[0])** 2 + (a[1]- b[1])** 2 + (a[-1]- b[-1]) ** 2)
** 0.5
I"m not sure there is any need to compute the sqrt here -- you can work
in units of "distance" squared, instead, since you are doing thresholds.
blue = (205, 241, 255) # a value I am presuming would be the average
blue in the image
That's not very blue!
Also, I don't think I followed your advice on
"processing at the image level rather than by looping over pixels" Would
you correct it for me?
See the recent thread "Remove Noise from a National Weather Service
Radar Image" to see how to convert to/from numpy arrays for this sort of
thing. I prefer that approach to imagemath, but that may be because I am
very familiar with numpy.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
chris.bar...@noaa.gov
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