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import cv2import matplotlib.pyplot as pltimport numpy as np%matplotlib inlinedef cv_show(name,img):    cv2.imshow(name,img)    cv2.waitKey(0)    cv2.destroyAllWindows()

1 图像梯度-Sobel算子

img = cv2.imread("pie.png",cv2.IMREAD_GRAYSCALE)cv_show("img",img)plt.imshow(img)

dst = cv2.Sobel(src, ddepth, dx, dy, ksize)

• ddepth:图像的深度

• dx和dy分别表示水平和竖直方向

• ksize是Sobel算子的大小

sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)sobelx = cv2.convertScaleAbs(sobelx)cv_show("sobelx",sobelx)plt.imshow(sobelx)

sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)sobely = cv2.convertScaleAbs(sobely)  cv_show("sobely",sobely)plt.imshow(sobely)

分别计算x和y，再求和

sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)cv_show("sobelxy",sobelxy)plt.imshow(sobelxy)

直接计算,效果一般般

sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)cv_show("sobelxy",sobelxy)plt.imshow(sobelxy)

# 例子img = cv2.imread("lena.jpg",cv2.IMREAD_GRAYSCALE)cv_show("img",img)plt.imshow(img)

sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)sobelx = cv2.convertScaleAbs(sobelx)sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)sobely = cv2.convertScaleAbs(sobely)sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)cv_show("sobelxy",sobelxy)plt.imshow(sobelxy)

2 图像梯度-Scharr算子

3 图像梯度-laplacian算子

#不同算子的差异img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)sobelx = cv2.convertScaleAbs(sobelx)   sobely = cv2.convertScaleAbs(sobely)  sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)  scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)scharry = cv2.Scharr(img,cv2.CV_64F,0,1)scharrx = cv2.convertScaleAbs(scharrx)   scharry = cv2.convertScaleAbs(scharry)  scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0) laplacian = cv2.Laplacian(img,cv2.CV_64F)laplacian = cv2.convertScaleAbs(laplacian)   res = np.hstack((sobelxy,scharrxy,laplacian))cv_show("res",res)

plt.imshow(res)

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