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Match colors of the second image to the colors of the first image.
"""Usage: python good.jpg bad.jpg save-corrected-as.jpg"""
from scipy.misc import imread, imsave
from scipy import mean, interp, ravel, array
from itertools import izip
import sys
def mkcurve(chan1,chan2):
"Calculate channel curve by averaging target values."
fst = lambda p: p[0]
snd = lambda p: p[1]
sums = {}
for v1, v2 in izip(ravel(chan1), ravel(chan2)):
old = sums.get(v1, [])
sums.update({v1: old + [v2]})
c = array( [ (src,mean(vals))
for src,vals in sorted(sums.iteritems()) ])
nvals = interp(range(256), c[:,0], c[:,1], 0, 255)
return dict(zip(range(256), nvals))
def correct_bad(good, bad):
"Match colors of the bad image to good image."
r, g, b = bad.transpose((2,0,1))
r2, g2, b2 = good.transpose((2,0,1))
rc = mkcurve(r,r2)
gc = mkcurve(g,g2)
bc = mkcurve(b,b2)
corr = bad.copy()
h, w = corr.shape[:2]
for row in xrange(h):
for col in xrange(w):
r,g,b = corr[row,col]
corr[row,col] = [rc[r], gc[g], bc[b]]
return corr
if __name__ == "__main__":
good, bad, saveas = sys.argv[1:1+3]
good = imread(good)
bad = imread(bad)
assert(good.shape == bad.shape)
corrected = correct_bad(good,bad)
imsave(saveas, corrected)
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