Conv.py

#Import needed libraries
import numpy as np 
import matplotlib.pyplot as plt 

# Create three images with different features
plain_img = np.array([np.array([100, 100]), np.array([100, 100])]) 
img_with_edge = np.array([np.array([100, 0]), np.array([100, 0])]) 

#Create a kernal to detect vertical edges (sobel, gradient edge detecting kernal)
kernel_vertical = np.array([np.array([2, -2]), np.array([2, -2])]) 

#Funtion to apply a kernal to an image
# elementwise multiplication followed by a sum 
def apply_kernel(img, kernel): 
	return True if(np.sum(np.multiply(img, kernel))!=0) else False 

#---------------------------------------------------------------------------------
# Plain Image (image with no edges)
plt.imshow(plain_img) 
plt.axis('off') 
plt.show() 

#Printing results of applying kernal 0 if an edge is not found
if apply_kernel(plain_img,kernel_vertical):
    print("An edge has been found")
else:
    print("No edges were found")
#---------------------------------------------------------------------------------
#Image with an edge
plt.imshow(img_with_edge) 
plt.axis('off') 
plt.show() 

#Printing results of applying kernal 0 if an edge is not found
if apply_kernel(img_with_edge,kernel_vertical):
    print("An edge has been found")
else:
    print("No edges were found")