Opencv 形态学操作 -python代码

import cv2
import numpy as np
import matplotlib as pl



def display(name,image):
    cv2.imshow(name,image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()



Vshow = cv2.imread("img/OIP-C.jpg")

gray = cv2.cvtColor(Vshow,cv2.COLOR_BGR2GRAY) # 灰度图

aussian = cv2.GaussianBlur(gray,(5,5),1) #高斯滤波
    
## 膨胀腐蚀 滤波 去除噪声
# ret ,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY) # 二值化
thresh = cv2.adaptiveThreshold(aussian,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,15,3)
    
#腐蚀 对的是二值化之后的数据
kernel = np.ones((3,3),np.uint8) # 3x3 的一个核
erosion = cv2.erode(thresh,kernel,iterations=2) ## 腐蚀一次
#膨胀
dige_dilate=cv2.dilate(erosion,kernel,iterations=2)
# 开运算 - 先腐蚀 后膨胀
opening= cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel)
# 闭运算 - 先膨胀 后腐蚀
closeing= cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel)
#梯度运算  -膨胀 减去 腐蚀
gradient = cv2.morphologyEx(thresh,cv2.MORPH_GRADIENT,kernel) 
#礼帽 和 黑帽
# 礼帽 - 原始输入 - 开运算
gradient = cv2.morphologyEx(thresh,cv2.MORPH_TOPHAT,kernel)

# 黑帽 - 闭运算-原始输入
gradient = cv2.morphologyEx(thresh,cv2.MORPH_BLACKHAT,kernel)

# 图像梯度 sobel 算子 分开计算要比合一起计算要好
#dstxy = cv2.Sobel(opening,cv2.CV_64F,1,1,ksize=3)
dstx = cv2.Sobel(opening,cv2.CV_64F,1,0,ksize=3)
dsty = cv2.Sobel(opening,cv2.CV_64F,0,1,ksize=3)

# 取绝对值
dstx = cv2.convertScaleAbs(dstx)
dsty = cv2.convertScaleAbs(dsty)
dstxy = cv2.addWeighted(dstx,0.5,dsty,0.5,0)

# scharr 算子 laplacian 算子


cv2.imshow("sobel",dstxy)

display("name",Vshow)