Question

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?

Answer 1

Surprising indeed.

You may want to use:

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?