A simple edge detection example

edge_detect.py

import cv2
import numpy as np

# define vertical and horizontal filters
N = 3
vertical_filter = [[-1,0,1],
                   [-1,0,1],
                   [-1,0,1]]
                   
horizontal_filter = [[-1,-1,-1],
                     [0,0,0],
                     [1,1,1]]
threshold = 100

# read in input image
im = cv2.imread('lenna.jpg', cv2.IMREAD_GRAYSCALE)
im_h = im.shape[0]
im_w = im.shape[1]

# create new images for edge detection results (without padding)
v_edge_im = np.zeros((im_h-N+1, im_w-N+1), dtype=np.uint8)
h_edge_im = np.zeros((im_h-N+1, im_w-N+1), dtype=np.uint8)

# slide over each location on the input image
for i in range(im_h):
    for j in range(im_w):
        # ignore 4 sides
        if i > im_h-N or j > im_w-N:
            continue
        
        # convolution
        v_conv_value = 0
        h_conv_value = 0
        for m  in range(N):
            for n in range(N):
                v_conv_value += im[i+m][j+n] * vertical_filter[m][n]
                h_conv_value += im[i+m][j+n] * horizontal_filter[m][n]
        if v_conv_value > threshold:
            v_edge_im[i][j] = 255
        else:
            v_edge_im[i][j] = 0
        
        if h_conv_value > threshold:
            h_edge_im[i][j] = 255
        else:
            h_edge_im[i][j] = 0

print(im.shape)
print(v_edge_im.shape)
print(h_edge_im.shape)

# show edge detection results
cv2.imshow('original', im)
cv2.imshow('vertical edge', v_edge_im)
cv2.imshow('horizontal edge', h_edge_im)
# exit after press any key
cv2.waitKey(0)