How to do it…

1. Import the necessary packages:

import sys 
import cv2 
import numpy as np 

2. Load the input image:

in_file = sys.argv[1] 
image = cv2.imread(in_file) 

3. Convert the RGB image into grayscale:

image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
cv2.imshow('Input grayscale image', image_gray) 

4. Regulate the histogram of the grayscale image:

image_gray_histoeq = cv2.equalizeHist(image_gray) 
cv2.imshow('Histogram equalized - grayscale image', image_gray_histoeq) 

5. Regulate the histogram of the RGB image:

image_yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV) 
image_yuv[:,:,0] = cv2.equalizeHist(image_yuv[:,:,0]) 
image_histoeq = cv2.cvtColor(image_yuv, cv2.COLOR_YUV2BGR) 

6. Display the output image:

cv2.imshow('Input image', image) 
cv2.imshow('Histogram equalized - color image', image_histoeq) 
cv2.waitKey()

7. The command used to execute the histogram.py Python program file, along with the input image (finger.jpg), is shown here:

8. The input image used to execute the histogram.py file is shown here:

9. The histogram equalized grayscale image obtained after executing the histogram.py file is shown here:

10. The histogram equalized color image obtained after executing the histogram.py file is shown here: