1. Import the Computer Vision package - cv2:

import cv2 
# Import Numerical Python package - numpy as np 
import numpy as np 

2. Read the image using the built-in imread function:

image = cv2.imread('image_6.jpg') 

3. Display the original image using the built-in imshow function:

cv2.imshow("Original", image) 

4. Wait until any key is pressed:

cv2.waitKey(0) 

5. Execute the pixel level action with the blurring operation:

# Blurring images: Averaging, cv2.blur built-in function 
# Averaging: Convolving image with normalized box filter 
# Convolution: Mathematical operation on 2 functions which produces third function. 
# Normalized box filter having size 3 x 3 would be: 
# (1/9)  [[1, 1, 1], 
#         [1, 1, 1], 
#         [1, 1, 1]] 
blur = cv2.blur(image,(9,9)) # (9 x 9) filter is used  

6. Display the blurred image:

cv2.imshow('Blurred', blur) 

7. Wait until any key is pressed:

cv2.waitKey(0)

8. Execute the pixel level action with the sharpening operation:

# Sharpening images: Emphasizes edges in an image 
kernel = np.array([[-1,-1,-1],  
                   [-1,9,-1],  
                   [-1,-1,-1]]) 
# If we don't normalize to 1, image would be brighter or darker respectively     
# cv2.filter2D is the built-in function used for sharpening images 
# cv2.filter2D(image, ddepth, kernel) 
# ddepth = -1, sharpened images will have same depth as original image 
sharpened = cv2.filter2D(image, -1, kernel) 

9. Display the sharpened image:

cv2.imshow('Sharpened', sharpened) 

10. Wait until any key is pressed:

cv2.waitKey(0) 

11. Terminate the program execution:

# Close all windows 
cv2.destroyAllWindows() 

12. The command used to execute the Blurring_Sharpening.py Python program file is shown here:

13. The input image used to execute the Blurring_Sharpening.py file is shown here:

14. The blurred image obtained after executing the Blurring_Sharpening.py file is shown here:

15. The sharpened image obtained after executing the Blurring_Sharpening.py file is shown here: