opencv 不同边缘提取算子提取效果对比

# -*- coding: utf-8 -*-
import cv2 as cv  
import numpy as np  
import matplotlib.pyplot as plt

#读取图像
img = cv.imread('d:/paojie.png')
img1 = cv.cvtColor(img, cv.COLOR_BGR2RGB)

#灰度化处理图像
grayImage = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

#高斯滤波
gaussianBlur = cv.GaussianBlur(grayImage, (3,3), 0)

#阈值处理
ret, binary = cv.threshold(gaussianBlur, 0, 255, cv.THRESH_BINARY+cv.THRESH_OTSU)

#Roberts算子
kernelx = np.array([[-1,0],[0,1]], dtype=int)
kernely = np.array([[0,-1],[1,0]], dtype=int)
x = cv.filter2D(binary, cv.CV_16S, kernelx)
y = cv.filter2D(binary, cv.CV_16S, kernely)
absX = cv.convertScaleAbs(x)     
absY = cv.convertScaleAbs(y)    
Roberts = cv.addWeighted(absX, 0.5, absY, 0.5, 0)

#Prewitt算子
kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
x = cv.filter2D(binary, cv.CV_16S, kernelx)
y = cv.filter2D(binary, cv.CV_16S, kernely)
absX = cv.convertScaleAbs(x)  
absY = cv.convertScaleAbs(y)    
Prewitt = cv.addWeighted(absX,0.5,absY,0.5,0)

#Sobel算子
x = cv.Sobel(binary, cv.CV_16S, 1, 0)
y = cv.Sobel(binary, cv.CV_16S, 0, 1)    
absX = cv.convertScaleAbs(x)   
absY = cv.convertScaleAbs(y)    
Sobel = cv.addWeighted(absX, 0.5, absY, 0.5, 0)

#拉普拉斯算法
dst = cv.Laplacian(binary, cv.CV_16S, ksize = 3)
Laplacian = cv.convertScaleAbs(dst) 

#效果图
titles = ['Source Image', 'Binary Image', 'Roberts Image',
          'Prewitt Image','Sobel Image', 'Laplacian Image']  
images = [img1, binary, Roberts, Prewitt, Sobel, Laplacian]  
for i in np.arange(6):  
   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
   plt.title(titles[i])  
   plt.xticks([]),plt.yticks([])  
plt.show()  

输出结果如图所示。其中，Laplacian算子对噪声比较敏感，由于其算法可能会出现双像素边界，常用来判断边缘像素位于图像的明区或暗区，很少用于边缘检测；Robert算子对陡峭的低噪声图像效果较好，尤其是边缘正负45度较多的图像，但定位准确率较差；Prewitt算子对灰度渐变的图像边缘提取效果较好，而没有考虑相邻点的距离远近对当前像素点的影响；Sobel算子考虑了综合因素，对噪声较多的图像处理效果更好。

表示各种边缘检测算子