M-V - 1 year ago 113
Python Question

Benchmarking Functions for Computing Average RGB Value of Images

I implemented computation of average RGB value of a Python Imaging Library image in 2 ways:

1 - using lists

``````def getAverageRGB(image):
"""
Given PIL Image, return average value of color as (r, g, b)
"""
# no. of pixels in image
npixels = image.size[0]*image.size[1]
# get colors as [(cnt1, (r1, g1, b1)), ...]
cols = image.getcolors(npixels)
# get [(c1*r1, c1*g1, c1*g2),...]
sumRGB = [(x[0]*x[1][0], x[0]*x[1][1], x[0]*x[1][2]) for x in cols]
# calculate (sum(ci*ri)/np, sum(ci*gi)/np, sum(ci*bi)/np)
# the zip gives us [(c1*r1, c2*r2, ..), (c1*g1, c1*g2,...)...]
avg = tuple([sum(x)/npixels for x in zip(*sumRGB)])
return avg
``````

2 - using numpy

``````def getAverageRGBN(image):
"""
Given PIL Image, return average value of color as (r, g, b)
"""
# get image as numpy array
im = np.array(image)
# get shape
w,h,d = im.shape
# change shape
im.shape = (w*h, d)
# get average
return tuple(np.average(im, axis=0))
``````

I was surprised to find that #1 runs about 20% faster than #2.

Am I using numpy correctly? Is there a better way to implement the average computation?

``````tuple(im.mean(axis=0))
to compute your mean `(r,g,b)`, but I doubt it's gonna improve things a lot. Have you tried to profile `getAverageRGBN` and find the bottleneck?