Histogram matching of two images in Python 2.x?

Question

I\'m trying to match the histograms of two images (in MATLAB this could be done using imhistmatch). Is there an equivalent function available from a standard Python library

Answer 1

Here is yet another implementation based on this and scikit-image exposure's cumulative_distribution function which uses np.interp similar to ali_m's implementation. The input and template image is assumed to be gray-scale and with pixel values as integers in [0,255].

from skimage.exposure import cumulative_distribution
import matplotlib.pylab as plt
import numpy as np

def cdf(im):
 '''
 computes the CDF of an image im as 2D numpy ndarray
 '''
 c, b = cumulative_distribution(im) 
 # pad the beginning and ending pixels and their CDF values
 c = np.insert(c, 0, [0]*b[0])
 c = np.append(c, [1]*(255-b[-1]))
 return c

def hist_matching(c, c_t, im):
 '''
 c: CDF of input image computed with the function cdf()
 c_t: CDF of template image computed with the function cdf()
 im: input image as 2D numpy ndarray
 returns the modified pixel values
 ''' 
 pixels = np.arange(256)
 # find closest pixel-matches corresponding to the CDF of the input image, given the value of the CDF H of   
 # the template image at the corresponding pixels, s.t. c_t = H(pixels) <=> pixels = H-1(c_t)
 new_pixels = np.interp(c, c_t, pixels) 
 im = (np.reshape(new_pixels[im.ravel()], im.shape)).astype(np.uint8)
 return im

The output is shown below: