Python Image Processing Cookbook

How to do it...

Perform the following steps to implement histogram matching:

1. Let's implement the histogram-matching algorithm with the hist_matching() function, which accepts the original and the template image's cdf along with the original image:

def hist_matching(c, c_t, im): b = np.interp(c, c_t, np.arange(256))             # find closest matches to b_t pix_repl = {i:b[i] for i in range(256)}              # dictionary to replace the pixels mp = np.arange(0,256) for (k, v) in pix_repl.items():  mp[k] = v s = im.shape im = np.reshape(mp[im.ravel()], im.shape) im = np.reshape(im, s) return im

2. Compute cdf of an image with the following function:

def cdf(im): c, b = cumulative_distribution(im) for i in range(b[0]):  c = np.insert(c, 0, 0) for i in range(b[-1]+1, ...