Mahotas – 获取标签边框

在本文中，我们将了解如何在 mahotas 中获取标记图像中的标签边界。为此，我们将使用来自核分割基准的荧光显微镜图像。我们可以在下面给出的命令的帮助下获取图像

mhotas.demos.nuclear_image()

下面是nuclear_image

为此，我们将使用 mahotas.labeled.borders 方法

Syntax : mahotas.labeled.borders(labelled_image)
Argument : It takes numpy.ndarray object as argument i.e labelled image
Return : It returns numpy.ndarray object i.e labelled image with only labels border 
 

注意： this 的输入应该是被标记的过滤图像对象
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]

示例 1：

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os
 
# loading nuclear image
f = mahotas.demos.load('nuclear')
 
# setting filter to the image
f = f[:, :, 0]
 
# setting gaussian filter
f = mahotas.gaussian_filter(f, 4)
 
# setting threshold value
f = (f> f.mean())
 
# creating a labelled image
labeled, n_nucleus = mahotas.label(f)
 
 
# showing the labelled image
print("Labelled Image")
imshow(labelled)
show()
 
# getting border of the labelled image
relabeled = mahotas.labelled.borders(labeled)
 
# showing the image
print("Labels with border")
imshow(relabelled)
show()

输出 ：

示例 2：

Python3

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
  
# loading image
img = mahotas.imread('dog_image.png')
    
# filtering the image
img = img[:, :, 0]
     
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 15)
  
# setting threshold value
gaussian = (gaussian > gaussian.mean())
  
# creating a labelled image
labelled, n_nucleus = mahotas.label(gaussian)
   
print("Labelled Image")
# showing the gaussian filter
imshow(labelled)
show()
  
# getting border of the labelled image
relabeled = mahotas.labelled.borders(labelled)
 
# showing the image
print("Labels with border")
imshow(relabelled)
show()

输出 ：