06-对图像进行腐蚀操作

形态学中的腐蚀操作一般处理的图像数据为二值的
cv2.erode(img,kernel,iterations = 1)
kernel表示拿多大的卷积核去腐蚀
iterations表示迭代次数
可以将一些带有毛毛的图像去毛毛化
原图

import cv2
import numpy as np

def show_photo(name,picture):
    cv2.imshow(name,picture)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

img = cv2.imread('E:Jupyter_workspacestudydata/jiaju.png')
show_photo('jiaju',img)

去毛毛

import cv2
import numpy as np

def show_photo(name,picture):
    cv2.imshow(name,picture)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

img = cv2.imread('E:Jupyter_workspacestudydata/jiaju.png')
kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
show_photo('erosion',erosion)

当然，这只是一个样例罢了
腐蚀操作其实就是对一些边缘进行剪切，处理之后的照片会相对变细
下面进行迭代次数的演示

import cv2
import numpy as np

def show_photo(name,picture):
    cv2.imshow(name,picture)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

pie = cv2.imread('E:Jupyter_workspacestudydata/pie.png')
show_photo('pie',pie)

原图：

import cv2
import numpy as np

def show_photo(name,picture):
    cv2.imshow(name,picture)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

pie = cv2.imread('E:Jupyter_workspacestudydata/pie.png')

kernel = np.ones((30,30),np.uint8)
erosion_1 = cv2.erode(pie,kernel,iterations = 1)
erosion_2 = cv2.erode(pie,kernel,iterations = 2)
erosion_3 = cv2.erode(pie,kernel,iterations = 3)
res = np.hstack((pie,erosion_1,erosion_2,erosion_3))

show_photo('YT-1-2-3',res)

不同的迭代次数的影响：