Computer vision: Image Gradients and Edge Detection using Laplacian 



import cv2

import numpy as np

from matplotlib import pyplot as plt


img = cv2.imread("sudoku.png", cv2.IMREAD_GRAYSCALE)

lap = cv2.Laplacian(img, cv2.CV_64F, ksize=3)

lap = np.uint8(np.absolute(lap))

sobelX = cv2.Sobel(img, cv2.CV_64F, 1, 0)

sobelY = cv2.Sobel(img, cv2.CV_64F, 0, 1)


sobelX = np.uint8(np.absolute(sobelX))

sobelY = np.uint8(np.absolute(sobelY))


sobelCombined = cv2.bitwise_or(sobelX, sobelY)


titles = ['image', 'Laplacian', 'sobelX', 'sobelY', 'sobelCombined']

images = [img, lap, sobelX, sobelY, sobelCombined]

for i in range(5):

    plt.subplot(2, 3, i+1), plt.imshow(images[i], 'gray')      

    plt.title(titles[i])      

    plt.xticks([]),plt.yticks([])#ticks      


plt.show() #show

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data used in this video



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