12、OpenCV Python 图像梯度

 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()