这篇教程Python+OpenCV 图像边缘检测四种实现方法写得很实用，希望能帮到您。
import cv2 as cvimport numpy as npimport 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.dllD:/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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