Mahotas – 图像椭圆轴

在本文中，我们将了解如何在 mahotas 中获取图像椭圆轴。 Ellipse exes 是图像椭圆的参数，是与原始图像具有相同质量和二阶矩的等强度椭圆的参数。
对于本教程，我们将使用“lena”图像，下面是加载 lena 图像的命令

mahotas.demos.load('lena')

下面是莉娜的图片

In order to do this we will use mahotas.features.ellipse_axes method
Syntax : mahotas.features.ellipse_axes(img)
Argument : It takes image object as argument
Return : It returns two float values 
 

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
import matplotlib.pyplot as plt
   
# loading image
img = mahotas.demos.load('lena')
   
# filtering image
img = img.max(2)
 
print("Image")
   
# showing image
imshow(img)
show()
 
# computing Parameters of the ‘image ellipse’
semimajor, semiminor = mahotas.features.ellipse_axes(img)
  
 
# showing value
print("Semi Major Exes : " + str(semimajor))
print("Semi Minor Exes : " + str(semiminor))

输出 ：

Image

Semi Major Exes : 295.60277400592844
Semi Minor Exes : 295.60277400592844

另一个例子

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import gray, imshow, show
import os
import matplotlib.pyplot as plt
  
# loading image
img = mahotas.imread('dog_image.png')
 
 
# filtering image
img = img[:, :, 0]
   
print("Image")
   
# showing image
imshow(img)
show()
# computing Parameters of the ‘image ellipse’
semimajor, semiminor = mahotas.features.ellipse_axes(img)
  
 
# showing value
print("Semi Major Exes : " + str(semimajor))
print("Semi Minor Exes : " + str(semiminor))

输出 ：

Image

Semi Major Exes : 508.79612573247636
Semi Minor Exes : 308.5809619544451