Thank you Stefan and Anne for such quick replies.
I am writing a gimp plugin, and if anybody is interested in how do that --
there are only about 10-20 examples that I"ve found -- this plugin is
attempting to do raster-to-vector conversion on the bluest pixels. It outputs
SVG and a python tuple. Current version is at
http://pastebin.com/m2bd8113f
I dunno np.histogram yet, so rather than look that up I tried Anne"s approach.
I get a divide by zero error on
np.divide(image[...,BLU], image[...,GRN], B)
...and I dont understand this well enough to diagnose. Any ideas?
Thanks in advance,
paul
----
Anne1 = """Well, I can see several approaches. The most direct way to do what
you're asking is to use scipy.optimize.bisect to implement the successive
approximations. That'll be almost as slow as your current approach, though.
Instead, I'd first write a function that measures the "blueness" of each
pixel:"""
def blueness(image, linedensity):
A = np.empty(image.shape[:-1], dtype=np.float32)
np.divide(image[...,BLU], image[...,RED], A) # use three-argument
# divide to reduce the number of float temporaries
B = np.empty(image.shape[:-1], dtype=np.float32)
np.divide(image[...,BLU], image[...,GRN], B)
return np.minimum(A, B)
Anne2 = """
Now, once you have the bluenesses, you can sort them and pull out the
blueness that gives you the percent you want:
"""
bluenesses = np.sort(blueness(image), axis=None) # axis=None flattens the
array
#factor = bluenesses[int((1-wanted_fraction)*len(bluenesses))]
w, h, bpp = image.shape
factor = bluenesses[int((1-(linedensity*6/float(w*h))) * len(bluenesses))]
Anne3 = """
If you want a smarter blueness filter, you could look into using HSV:
you could then specify a distance in hue and a distance in saturation,
based on how relatively important you think they are.
Anne
"""
print "blueness returning factor = %r" % (factor)
return factor
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