04计算机视觉-opencv图像梯度处理

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#04计算机视觉-opencv图像梯度处理 #计算机视觉 #opencv图像梯度 #图像梯度处理

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本文详细介绍了使用Sobel算子、Scharr算子和Laplacian算子进行图像梯度计算的方法，展示了如何通过这些算子检测图像中的边缘，并比较了不同算子在边缘检测效果上的差异。

import cv2
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

1 图像梯度-Sobel算子

[外链图片转存失败(img-kxijQeIx-1565701492632)(sobel_1.png)]

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

cv_show("img",img)
plt.imshow(img)

<matplotlib.image.AxesImage at 0x1f6e49302b0>

[外链图片转存失败(img-hEQMo4sB-1565701492633)(output_3_1.png)]

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)

<matplotlib.image.AxesImage at 0x1f6e7a06978>

[外链图片转存失败(img-50eXFf9D-1565701492633)(output_5_1.png)]

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

cv_show("sobely",sobely)
plt.imshow(sobely)

<matplotlib.image.AxesImage at 0x1f6f7cb80f0>

[外链图片转存失败(img-DCpuPjrW-1565701492634)(output_6_1.png)]

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)

<matplotlib.image.AxesImage at 0x1f6f78cd400>

[外链图片转存失败(img-TrDMMDed-1565701492635)(output_8_1.png)]

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)

<matplotlib.image.AxesImage at 0x1f6e3f0f208>

[外链图片转存失败(img-3EnyfMVq-1565701492636)(output_10_1.png)]

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

<matplotlib.image.AxesImage at 0x1f6e441b048>

[外链图片转存失败(img-Ht1p96dO-1565701492636)(output_11_1.png)]

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)

<matplotlib.image.AxesImage at 0x1f6e77a0668>

[外链图片转存失败(img-1lri2PUd-1565701492637)(output_12_1.png)]

2 图像梯度-Scharr算子

[外链图片转存失败(img-hDqDwSGQ-1565701492638)(scharr.png)]

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)

<matplotlib.image.AxesImage at 0x1f6e78c1780>

[外链图片转存失败(img-KPLXzyD0-1565701492640)(output_18_1.png)]