Mahotas – 与二值图像的距离

在本文中，我们将了解如何在 mahotas 中获取二值图像的距离图。距离变换，也称为距离图或距离场，是数字图像的派生表示。术语的选择取决于对所讨论对象的观点：初始图像是否被转换为另一种表示，或者它只是被赋予了额外的地图或场。
为此，我们将使用 mahotas.distance 方法

Syntax : mahotas.distance(img)
Argument : It takes image object which should be binary as argument
Return : It returns image object 
 

注意：输入图像应该是二进制图像，也可以标记，图像应该被过滤或加载为灰色以使其成为二进制
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 as mh
import numpy as np
from pylab import imshow, show
  
# creating region
# numpy.ndarray
regions = np.zeros((10, 10), bool)
  
# setting 1 value to the region
regions[:3, :3] = 1
regions[6:, 6:] = 1
  
# getting labeled function
labeled, nr_objects = mh.label(regions)
  
# showing the image with interpolation = 'nearest'
print("Image")
imshow(labeled, interpolation ='nearest')
show()
 
# getting distance map
dmap = mahotas.distance(labeled)
 
# showing image
print("Distance Map")
imshow(dmap)
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 labeled image
labeled, n_nucleus = mahotas.label(gaussian)
  
 
print("Image")
# showing the gaussian filter
imshow(labeled)
show()
 
 
# getting distance map
dmap = mahotas.distance(labeled)
 
# showing image
print("Distance Map")
imshow(dmap)
show()

输出 ：