detect_edges.py

#read in the image
img = plt.imread('penguin.jpg')

#convert image to grayscale
greyImg = np.mean(img, axis=2).astype(np.uint8)

#the edge kernel, when convolved with our image, detects the edges
edge_kernel = np.array([[0,1,0],[1,-4,1],[0,1,0]])

#convolve edge kernel with image
edges_img = convolve(greyImg, edge_kernel, mode='valid')

#normalize
edges_img = (edges_img - edges_img.min())/(edges_img.max() - edges_img.min())

#only keep pixels which are a certain threshold away from the mean
tol = np.mean(edges_img) - 2.5*np.std(edges_img)

#polarize the pixels in the edges image to either 0 or 1
mask = edges_img < tol
edges_img[mask] = 0
edges_img[~mask] = 1

#show the polarized edges image
plt.imshow(edges_img, cmap='gray')