convolution.py

import numpy as np
import matplotlib.pyplot as plt

# let img1 be an image with no features
img1 = np.array([np.array([200, 200]), np.array([200, 200])])
print(img1)
img2 = np.array([np.array([200, 200]), np.array([0, 0])])
img3 = np.array([np.array([200, 0]), np.array([200, 0])])

kernel_horizontal = np.array([np.array([2, 2]), np.array([-2, -2])])
print(kernel_horizontal, 'is a kernel for detecting horizontal edges')

kernel_vertical = np.array([np.array([2, -2]), np.array([2, -2])])
print(kernel_vertical, 'is a kernel for detecting vertical edges')

# We will apply the kernels on the images by
# elementwise multiplication followed by summation
def apply_kernel(img, kernel):
	return np.sum(np.multiply(img, kernel))

# Visualizing img1
plt.imshow(img1)
plt.axis('off')
plt.title('img1')
plt.show()

# Checking for horizontal and vertical features in image1
print('Horizontal edge confidence score:', apply_kernel(img1,
											kernel_horizontal))
print('Vertical edge confidence score:', apply_kernel(img1,
											kernel_vertical))

# Visualizing img2
plt.imshow(img2)
plt.axis('off')
plt.title('img2')
plt.show()

# Checking for horizontal and vertical features in image2
print('Horizontal edge confidence score:', apply_kernel(img2,
											kernel_horizontal))
print('Vertical edge confidence score:', apply_kernel(img2,
											kernel_vertical))

# Visualizing img3
plt.imshow(img3)
plt.axis('off')
plt.title('img3')
plt.show()

# Checking for horizontal and vertical features in image3
print('Horizontal edge confidence score:', apply_kernel(img3,
											kernel_horizontal))
print('Vertical edge confidence score:', apply_kernel(img3,
											kernel_vertical))