diff --git a/边缘检测.py b/边缘检测.py
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+++ b/边缘检测.py
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+import cv2  
+import numpy as np
+
+#LoG算子的运行速度较慢，请耐心等待
+
+#边缘强化
+CRH = cv2.imread("da.jpg",0)  
+CRH = CRH.astype('float')  
+row, column = CRH.shape  
+gradient = np.zeros((row, column))  
+for x in range(row - 1):  
+    for y in range(column - 1):  
+        gx = abs(CRH[x + 1, y] - CRH[x, y])  
+        gy = abs(CRH[x, y + 1] - CRH[x, y])  
+        gradient[x, y] = gx + gy  
+  
+sharp = CRH + gradient     
+sharp = np.where(sharp > 255, 255, sharp)  
+sharp = np.where(sharp < 0, 0, sharp)  
+# 数据类型变换  
+gradient = gradient.astype('uint8')  
+sharp = sharp.astype('uint8')  
+cv2.imshow('边缘强化',gradient) 
+cv2.waitKey(0)    
+
+
+
+#Roberts算子   
+img = cv2.imread("da.jpg",0)
+img = cv2.merge([img,img,img])      
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
+# 2. Roberts算子
+kernelx = np.array([[-1, 0], [0, 1]], dtype=int)  
+kernely = np.array([[0, -1], [1, 0]], dtype=int)  
+# 3. 卷积操作
+x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)  
+y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)  
+# 4. 数据格式转换
+grayImage = cv2.convertScaleAbs(grayImage)
+absX = cv2.convertScaleAbs(x)  
+absY = cv2.convertScaleAbs(y)  
+Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)  
+cv2.imshow('Roberts算子',Roberts) 
+cv2.waitKey(0)    
+
+
+
+#Sobel 算子
+# 1. 灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
+# 2. 求Sobel 算子
+kernelx = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0)  
+kernely = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1)
+x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)  
+y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)    
+# 3. 数据格式转换
+absX = cv2.convertScaleAbs(x)  
+absY = cv2.convertScaleAbs(y)  
+# 4. 组合图像
+Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+cv2.imshow('Sobel算子',Sobel) 
+cv2.waitKey(0)    
+
+
+
+#Laplacian 算子
+# 1. 灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
+# 2. 高斯滤波
+imagedo = cv2.GaussianBlur(grayImage,(5,5),0,0)
+# 3. 拉普拉斯算法
+dst = cv2.Laplacian(imagedo, cv2.CV_16S, ksize=3) 
+# 4. 数据格式转换
+Laplacian = cv2.convertScaleAbs(dst)
+cv2.imshow('Laplacian',Laplacian) 
+cv2.waitKey(0)    
+
+
+
+#LoG 边缘算子
+image = cv2.copyMakeBorder(img, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
+image = cv2.GaussianBlur(image,(3,3),0,0)
+image1 = np.zeros(shape=image.shape,dtype=np.int16)
+# 使用Numpy定义LoG算子
+m1 = np.array([[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1 ,0], [0, 0, -1, 0, 0]])
+#  卷积运算
+# 为了使卷积对每个像素都进行运算，原图像的边缘像素要对准模板的中心。
+# 由于图像边缘扩大了2像素，因此要从位置2到行(列)-2
+rows = image.shape[0]
+cols = image.shape[1]
+for i in range(2, rows - 2):
+    for j in range(2, cols - 2):
+            image1[i, j] = np.sum(m1 * image[i - 2:i + 3, j - 2:j + 3, 1])   
+image1 = cv2.convertScaleAbs(image1) 
+cv2.imshow('LoG 边缘算子',image1) 
+cv2.waitKey(0)
+
+
+
+#Canny 算子
+# 1. 高斯滤波
+blur = cv2.GaussianBlur(img, (3, 3), 0) 
+# 2. 灰度转换
+grayImage = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)  
+# 3. 求x，y方向的Sobel算子
+gradx = cv2.Sobel(grayImage, cv2.CV_16SC1, 1, 0)  
+grady = cv2.Sobel(grayImage, cv2.CV_16SC1, 0, 1)  
+# 4. 使用Canny函数处理图像，x,y分别是3求出来的梯度，低阈值50，高阈值150
+edge_output = cv2.Canny(gradx, grady, 50, 150)
+cv2.imshow('Canny算子',edge_output) 
+cv2.waitKey(0)
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