1 __author__ = "WSX" 2 import cv2 as cv 3 import numpy as np 4  5 def lapalian_demo(image):  #拉普拉斯算子 6     #dst = cv.Laplacian(image, cv.CV_32F) #内置函数来实现 7     #lpls = cv.convertScaleAbs(dst) 8     kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])  #自定义来实现 9     dst = cv.filter2D(image, cv.CV_32F, kernel=kernel)10     lpls = cv.convertScaleAbs(dst)11     cv.imshow("lapalian_demo", lpls)12 13 14 def sobel_demo(image): #sobel算子15     grad_x = cv.Scharr(image, cv.CV_32F, 1, 0) #x的一阶导数16     grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)17     gradx = cv.convertScaleAbs(grad_x)  #  先绝对值  再转到8位图像上18     grady = cv.convertScaleAbs(grad_y)19     cv.imshow("gradient-x", gradx)  #左右有差异的表现20     cv.imshow("gradient-y", grady)  #上下有差异的表现21     gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)  #一起表现22     cv.imshow("gradient", gradxy)23 24 def main():25     img = cv.imread("1.JPG")26     cv.namedWindow("Show", cv.WINDOW_AUTOSIZE)27     cv.imshow("Show", img)28     sobel_demo(img)29 30     cv.waitKey(0)31     cv.destroyAllWindows()32 33 main()