Technical Notes - The Python Imaging Library
	Copyright © 1997 by Fredrik Lundh <fredrik@pythonware.com>
	Updated 17 Aug 1997 (Created 21 May 1997)

Histogram Equalization (Note #4)

Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.

Figure: original image, with corresponding histogram

Figure: same image, after equalization

Source

# histogram equalization

import operator

def equalize(h):

    lut = []

    for b in range(0, len(h), 256):

        # step size
        step = reduce(operator.add, h[b:b+256]) / 255

        # create equalization lookup table
        n = 0
        for i in range(256):
            lut.append(n / step)
            n = n + h[i+b]

    return lut

#
# test stuff

if __name__ == "__main__":

    import Image

    im = Image.open("/usr/iv/tip/images/clenna.im")

    # calculate lookup table
    lut = equalize(im.histogram())

    # map image through lookup table
    im = im.point(lut)

    im.save("out.ppm")