Python+OpenCV 图像边缘检测四种实现方法
投稿：newname
本文主要介绍了通过OpenCV中Sobel算子、Schaar算子、Laplacian算子以及Canny分别实现图像边缘检测并总结了四者的优缺点，感兴趣的同学可以参考一下
import cv2 as cv import numpy as np import matplotlib.pyplot as plt # 设置兼容中文 plt.rcParams['font.family'] = ['sans-serif'] plt.rcParams['font.sans-serif'] = ['SimHei']
D:\Anaconda\AZWZ\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\AZWZ\lib\site-packages\numpy\.libs\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll
D:\Anaconda\AZWZ\lib\site-packages\numpy\.libs\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll
warnings.warn("loaded more than 1 DLL from .libs:\n%s" %
horse = cv.imread('img/horse.jpg',0)
plt.imshow(horse,cmap=plt.cm.gray)
plt.imshow(horse,cmap=plt.cm.gray)
1.Sobel算子
# 1,0 代表沿x方向做sobel算子 x = cv.Sobel(horse,cv.CV_16S,1,0) # 0,1 代表沿y方向做sobel算子 y = cv.Sobel(horse,cv.CV_16S,0,1)
# 格式转换 absx = cv.convertScaleAbs(x) absy = cv.convertScaleAbs(y)
# 边缘检测结果 res = cv.addWeighted(absx,0.5,absy,0.5,0)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
m1 = plt.imshow(horse,cmap=plt.cm.gray)
plt.title("原图")
plt.subplot(1,2,2)
m2 = plt.imshow(res,cmap=plt.cm.gray)
plt.title("Sobel算子边缘检测")
Text(0.5, 1.0, 'Sobel算子边缘检测')
2.Schaar算子(更能体现细节)
# 1,0 代表沿x方向做sobel算子 x = cv.Sobel(horse,cv.CV_16S,1,0,ksize=-1) # 0,1 代表沿y方向做sobel算子 y = cv.Sobel(horse,cv.CV_16S,0,1,ksize=-1)
# 格式转换 absx = cv.convertScaleAbs(x) absy = cv.convertScaleAbs(y)
# 边缘检测结果 res = cv.addWeighted(absx,0.5,absy,0.5,0)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
m1 = plt.imshow(horse,cmap=plt.cm.gray)
plt.title("原图")
plt.subplot(1,2,2)
m2 = plt.imshow(res,cmap=plt.cm.gray)
plt.title("Schaar算子边缘检测")
Text(0.5, 1.0, 'Schaar算子边缘检测')
3.Laplacian算子(基于零穿越的，二阶导数的0值点)
res = cv.Laplacian(horse,cv.CV_16S)
res = cv.convertScaleAbs(res)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
m1 = plt.imshow(horse,cmap=plt.cm.gray)
plt.title("原图")
plt.subplot(1,2,2)
m2 = plt.imshow(res,cmap=plt.cm.gray)
plt.title("Laplacian算子边缘检测")
Text(0.5, 1.0, 'Laplacian算子边缘检测')
4.Canny边缘检测(被认为是最优的边缘检测算法)
res = cv.Canny(horse,0,100)
# res = cv.convertScaleAbs(res) Canny边缘检测是一种二值检测，不需要转换格式这一个步骤
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
m1 = plt.imshow(horse,cmap=plt.cm.gray)
plt.title("原图")
plt.subplot(1,2,2)
m2 = plt.imshow(res,cmap=plt.cm.gray)
plt.title("Canny边缘检测")
Text(0.5, 1.0, 'Canny边缘检测')
总结
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