Mahotas – 条件侵蚀图像

在本文中，我们将了解如何在 mahotas 中对图像进行条件腐蚀。侵蚀是形态学图像处理中的两个基本操作之一（另一个是膨胀），所有其他形态学操作都基于该操作。它最初是为二值图像定义的，后来扩展到灰度图像，随后扩展到完整的格子。

在本教程中，我们将使用“lena”图像，下面是加载它的命令。

mahotas.demos.load('lena')

下面是莉娜的图片

In order to do this we will use mahotas.cerode method
Syntax : mahotas.cerode(img, c_grey)
Argument : It takes image object, conditional image as argument
Return : It returns image object 
 

注意：输入图像应被过滤或应加载为灰色

为了过滤图像，我们将获取图像对象 numpy.ndarray 并在索引的帮助下对其进行过滤，下面是执行此操作的命令

image = image[:, :, 0]

下面是实现

Python3

# importing required libraries
import mahotas
import mahotas.demos
from pylab import gray, imshow, show
import numpy as np
   
# loading image
img = mahotas.demos.load('lena')
  
# grey image
g = img[:, :, 1]
  
# multiplying grey image values
g = g * 3
 
# filtering image
img = img.max(2)
   
# otsu method
T_otsu = mahotas.otsu(img)  
   
# image values should be greater than otsu value
img = img > T_otsu
   
print("Image threshold using Otsu Method")
   
# showing image
imshow(img)
show()
   
# eroding image using conditional grey image
new_img = mahotas.cerode(img, g)
   
# showing eroded image
print("Eroded Image")
imshow(new_img)
show()

输出 ：

Image threshold using Otsu Method

Eroded Image

另一个例子

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
  
# loading image
img = mahotas.imread('dog_image.png')
 
# getting grey image
g = img[:, :, 0]
 
  
# multiplying grey image values
g = g * 2
 
# filtering image
img = img[:, :, 0]
   
# otsu method
T_otsu = mahotas.otsu(img)  
   
# image values should be greater than otsu value
img = img > T_otsu
   
print("Image threshold using Otsu Method")
   
# showing image
imshow(img)
show()
   
# eroding image using conditional grey image
new_img = mahotas.cerode(img, g)
   
# showing eroded image
print("Eroded Image")
imshow(new_img)
show()

输出 ：

Image threshold using Otsu Method

Eroded Image