In Chapter 1, Image Manipulation and Transformation, we saw how the contrast stretching operation can be used to increase the contrast of an image. However, it is just a linear scaling function that is applied to image pixel values, and hence the image enhancement is less drastic than its more sophisticated counterpart, histogram equalization. This recipe will show how to implement contrast stretching using the histogram equalization. It is also a point transform that uses a non-linear mapping that reassigns the pixel intensity values in the input image in such a way that the output image has a uniform distribution of intensities (a flat histogram), and thereby enhances the contrast of the image.

In this recipe, we will implement histogram equalization with our own function, and also use scikit-image's global and local (adaptive) histogram equalization functions, starting with an RGB image. As usual, let's start by importing the required libraries:

import numpy as np
import matplotlib.pylab as plt
from skimage.io import imread
from skimage.exposure import equalize_hist, equalize_adapthist

To execute this recipe, perform the following steps:

1. Define the plot_image() and plot_hist() functions to display an image and return its cumulative distribution function (cdf), respectively:

def plot_image(image, title):
 plt.imshow(image)
 plt.title(title, size=20)
 plt.axis('off')
 
def plot_hist(img):
 colors = ['r', 'g', 'b']
 cdf = np.zeros((256,3))
 for i in range(3):
  hist, bins = np.histogram(img[...,i].flatten(),256,[0,256], \
                normed=True)
  cdf[...,i] = hist.cumsum()
  cdf_normalized = cdf[...,i] * hist.max() / cdf.max()
  plt.plot(cdf_normalized, color = colors[i], \
                label='cdf ({})'.format(colors[i]))
  binWidth = bins[1] - bins[0]
  plt.bar(bins[:-1], hist*binWidth, binWidth, 
                label='hist ({})'.format(colors[i]))
  plt.xlim([0,256])
  plt.legend(loc = 'upper left')
 return cdf

2. Implement the histogram equalization by reassigning the pixel values with the corresponding cdf value for that pixel using the following code:

img = imread('images/train.png')
cdf = plot_hist(img)
img2 = np.copy(img)
for i in range(3):
 cdf_m = np.ma.masked_equal(cdf[...,i],0)
 cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min()) 
            # min-max normalize
 cdf2 = np.ma.filled(cdf_m,0).astype('uint8')
 img2[...,i] = cdf2[img[...,i]]

3. Perform histogram equalization with the same input image using scikit-image library functions that implement global and adaptive histogram equalization:

equ1 = (255*equalize_hist(img)).astype(np.uint8)
equ2 = (255*equalize_adapthist(img)).astype(np.uint8)

For each image channel, each pixel value needs to be reassigned to the corresponding cdf value for the pixel, as shown in the following diagram:

The equalize_hist() and equalize_adapthist() functions from the scikit-image.exposure module were used to get the globally and locally contrast-enhanced images, respectively.

If you run the preceding code and plot the input and output images along with the corresponding histograms and cdfs, you will get the following output (the screenshot is just some part of the output):

Use the createCLAHE() function from OpenCV-Python to perform adaptive histogram equalization. Compare the quality of the output results obtained using different implementations. Also, run with a few different low-contrast images.