- Import the Computer Vision package - cv2:
import cv2 # Import Numerical Python package - numpy as np import numpy as np
- Read the image using the built-in imread function:
image = cv2.imread('image_6.jpg')
- Display the original image using the built-in imshow function:
cv2.imshow("Original", image)
- Wait until any key is pressed:
cv2.waitKey(0)
- 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
- Display the blurred image:
cv2.imshow('Blurred', blur)
- Wait until any key is pressed:
cv2.waitKey(0)
- 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)
- Display the sharpened image:
cv2.imshow('Sharpened', sharpened)
- Wait until any key is pressed:
cv2.waitKey(0)
- Terminate the program execution:
# Close all windows cv2.destroyAllWindows()
- The command used to execute the Blurring_Sharpening.py Python program file is shown here:
- The input image used to execute the Blurring_Sharpening.py file is shown here:
- The blurred image obtained after executing the Blurring_Sharpening.py file is shown here:
- The sharpened image obtained after executing the Blurring_Sharpening.py file is shown here: