OpenCV | 图像梯度sobel算子、scharr算子、lapkacian算子

import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
%matplotlib inline

1、sobel算子

img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE)
cv2.imshow('img',img)
cv2.waitKey()
cv2.destroyAllWindows()

pie图片

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

def cv_show(img,name):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()

sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
cv_show(sobelx,'sobelx')

#白到黑是正数，黑道白就是负数了，所有负数会被截断成0，所以要取绝对值

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

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

运行完图片

2、图像梯度-Scharr算子图像梯度-laplacian算子

scharr算子：

laplacian算子：

原图片

灰度化：

img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
cv_show(img,'img')

lena图片

#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')

运行完图片