Code

#CODE

import cv2
from cv2 import imshow
import matplotlib.pyplot as plt

#reading the image
#u have to take the image with the same name and format,
img=cv2.imread("your image.jpg")

plt.figure(figsize=(14,8))
#ORDINARY IMAGE
plt.subplot(1,2,1)
plt.title('Original image', size=18)
plt.axis('off')

#changing normal image into RGB image
RGB_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#To show image below of ordinary image
plt.imshow(RGB_img)
#SKETCH
plt.subplot(1,2,2)
plt.title('Sketch', size=18)

#Converting img into RGB
RGB_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#RGB into Grey
grey_img=cv2.cvtColor(RGB_img, cv2.COLOR_BGR2GRAY)

#Inverting grey img
invert_img=cv2.bitwise_not(grey_img)

'''
Bluring Inverted image by GAUSSIAN BLUR 
For creating sketch, we require only the prominent features (contrasting edges) from the image.
For small images, kernel size of (3,3), (5,5) etc. will be sufficient.
For large images,kernal size will obtain by using trial and error method.
'''

blur_img=cv2.GaussianBlur(invert_img, (111,111),0)

#Again Inverting blur image
invblur_img=cv2.bitwise_not(blur_img)

'''
now The sketch can be obtained by performing bit-wise division between 
the grayscale image and the inverted-blurred image.
'''
sketch_img=cv2.divide(grey_img,invblur_img, scale=256.0)

#Converting sketch into rgb sketch to add some black and white colours perfectly.
rgb_sketch=cv2.cvtColor(sketch_img, cv2.COLOR_BGR2RGB)
plt.imshow(rgb_sketch)
plt.axis('off')
plt.show()