How to do it...

Use the following steps:

1. Import all necessary modules:

import cv2
import numpy as np
import matplotlib.pyplot as plt

2. Load the image as grayscale and display it:

grey = cv2.imread('../data/Lena.png', 0)
cv2.imshow('original grey', grey)
cv2.waitKey()
cv2.destroyAllWindows()

3. Equalize the histogram of the grayscale image:

grey_eq = cv2.equalizeHist(grey)

4. Compute the histogram for the equalized image and show it:

hist, bins = np.histogram(grey_eq, 256, [0, 255])
plt.fill_between(range(256), hist, 0)
plt.xlabel('pixel value')
plt.show()

5. Show the equalized image:

cv2.imshow('equalized grey', grey_eq)
cv2.waitKey()
cv2.destroyAllWindows()

6. Load the image as BGR and convert it to the HSV color space:

color = cv2.imread('../data/Lena.png')
hsv = cv2.cvtColor(color, cv2.COLOR_BGR2HSV)

7. Equalize the V channel of the HSV image and convert it back to the RGB color space:

hsv[..., 2] = cv2.equalizeHist(hsv[..., 2])
color_eq = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
cv2.imshow('original color', color)

8. Show the equalized full color image:

cv2.imshow('equalized color', color_eq)
cv2.waitKey()
cv2.destroyAllWindows()