边缘检测代码

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一、canny算子

1.原理介绍

https://www.cnblogs.com/king-lps/p/8007134.html

2.canny算子函数

import matplotlib.pyplot as plt
import numpy as np
import math
import cv2
from PIL import Image
img=cv2.imread("C:/Users/S5/Desktop/PIC/case1/mohu1.png",cv2.IMREAD_GRAYSCALE)
r=cv2.Canny(img,50,80)
cv2.imshow('orignal',img)
cv2.imshow("result",r)
cv2.waitKey()
cv2.destroyAllWindows()
saveFile = "C:/Users/S5/Desktop/PIC/jieguo.jpg" 
cv2.imwrite(saveFile,r)
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3.canny算子程序

import matplotlib.pyplot as plt
import numpy as np
import math
import cv2
img=plt.imread("C:/Users/S5/Desktop/PIC/case1/mohu1.png")
sigma1 = sigma2 = 1
sum = 0
gaussian = np.zeros([5, 5])
for i in range(5):
    for j in range(5):
        gaussian[i,j] = math.exp(-1/2 * (np.square(i-3)/np.square(sigma1)           #生成二维高斯分布矩阵
                        + (np.square(j-3)/np.square(sigma2)))) / (2*math.pi*sigma1*sigma2)
        sum = sum + gaussian[i, j]
gaussian = gaussian/sum
print(gaussian)
def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
gray = rgb2gray(img)
W, H = gray.shape
new_gray = np.zeros([W-5, H-5])
for i in range(W-5):
    for j in range(H-5):
        new_gray[i,j] = np.sum(gray[i:i+5,j:j+5]*gaussian)
plt.imshow(new_gray, cmap="gray")
W1, H1 = new_gray.shape
dx = np.zeros([W1-1, H1-1])
dy = np.zeros([W1-1, H1-1])
d = np.zeros([W1-1, H1-1])
for i in range(W1-1):
    for j in range(H1-1):   
        dx[i,j] = new_gray[i, j+1] - new_gray[i, j]
        dy[i,j] = new_gray[i+1, j] - new_gray[i, j]        
        d[i, j] = np.sqrt(np.square(dx[i,j]) + np.square(dy[i,j])) 
plt.imshow(d, cmap="gray")
W2, H2 = d.shape
NMS = np.copy(d)
NMS[0,:] = NMS[W2-1,:] = NMS[:,0] = NMS[:, H2-1] = 0
for i in range(1, W2-1):
    for j in range(1, H2-1):
        
        if d[i, j] == 0:
            NMS[i, j] = 0
        else:
            gradX = dx[i, j]
            gradY = dy[i, j]
            gradTemp = d[i, j]
            
            # 如果Y方向幅度值较大
            if np.abs(gradY) > np.abs(gradX):
                weight = np.abs(gradX) / np.abs(gradY)
                grad2 = d[i-1, j]
                grad4 = d[i+1, j]
                # 如果x,y方向梯度符号相同
                if gradX * gradY > 0:
                    grad1 = d[i-1, j-1]
                    grad3 = d[i+1, j+1]
                # 如果x,y方向梯度符号相反
                else:
                    grad1 = d[i-1, j+1]
                    grad3 = d[i+1, j-1]
                    
            # 如果X方向幅度值较大
            else:
                weight = np.abs(gradY) / np.abs(gradX)
                grad2 = d[i, j-1]
                grad4 = d[i, j+1]
                # 如果x,y方向梯度符号相同
                if gradX * gradY > 0:
                    grad1 = d[i+1, j-1]
                    grad3 = d[i-1, j+1]
                # 如果x,y方向梯度符号相反
                else:
                    grad1 = d[i-1, j-1]
                    grad3 = d[i+1, j+1]
    
            gradTemp1 = weight * grad1 + (1-weight) * grad2
            gradTemp2 = weight * grad3 + (1-weight) * grad4
            if gradTemp >= gradTemp1 and gradTemp >= gradTemp2:
                NMS[i, j] = gradTemp
            else:
                NMS[i, j] = 0
plt.imshow(NMS, cmap = "gray")
W3, H3 = NMS.shape
DT = np.zeros([W3, H3])
TL = 0.65 * np.max(NMS)
TH = 0.8 * np.max(NMS)
for i in range(1, W3-1):
    for j in range(1, H3-1):
        if (NMS[i, j] < TL):
            DT[i, j] = 0
        elif (NMS[i, j] > TH):
            DT[i, j] = 1
        elif ((NMS[i-1, j-1:j+1] < TH).any() or (NMS[i+1, j-1:j+1]).any() 
              or (NMS[i, [j-1, j+1]] < TH).any()):
            DT[i, j] = 1
plt.imshow(DT, cmap = "gray")
cv2.imwrite("C:/Users/S5/Desktop/PIC/canny.jpg",DT)  
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二、sobel算子

import matplotlib.pyplot as plt
import numpy as np
import math
import cv2
img = cv2.imread('C:/Users/S5/Desktop/PIC/case1/mohu1.png',cv2.IMREAD_GRAYSCALE)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()
#定义一个显示函数
def cv_show(img,name):
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()
#取横向轮廓
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)
cv_show(sobelx,'sobelx')
#取纵向轮廓
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = cv2.convertScaleAbs(sobely)  
cv_show(sobely,'sobely')
#横纵轮廓叠加
sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
cv_show(sobelxy,'sobelxy')
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