Mahotas – Zernike 功能

在本文中，我们将了解如何在 mahotas 中获取给定图像的 zernike 特征。 Zernike 多项式是正交基组（一组函数，其中任何对函数的乘积的积分为零）
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.zernike method
Syntax : mahotas.features.zernike(img, degree, radius)
Argument : It takes image object and two integer as argument
Return : It returns 1-D array 
 

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()
 
# degree
degree = 10
 
# radius
radius = 10
 
# computing zernike feature
value = mahotas.features.zernike(img, degree, radius)
  
 
# printing value
print(value)

输出 ：

Image

[0.31830989 0.01261485 0.00614926 0.00769591 0.0097145  0.01757332
 0.00617458 0.01008905 0.01415304 0.01099679 0.02894761 0.01838737
 0.0074247  0.01333135 0.01958184 0.00431827 0.00540781 0.01675913
 0.03511082 0.00699177 0.00357231 0.01593838 0.01621848 0.0240565
 0.0154929  0.01631347 0.03239474 0.02506811 0.00796528 0.01291179
 0.01198231 0.01916542 0.0165929  0.01032658 0.02028499 0.02506003]

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()
 
# degree
degree = 10
 
# radius
radius = 10
 
# computing zernike feature
value = mahotas.features.zernike(img, degree, radius)
  
 
# printing value
print(value)

输出 ：

Image

[0.31830989 0.00985427 0.00714652 0.00171408 0.00442245 0.01796711
 0.00716781 0.00179965 0.0039829  0.0031081  0.02447476 0.0011686
 0.009291   0.00174885 0.00357579 0.00692029 0.0043969  0.03528869
 0.00264739 0.01381883 0.00750501 0.0036528  0.00867514 0.01298398
 0.0129556  0.00602334 0.04108562 0.00377269 0.01859098 0.01109795
 0.00178511 0.0082474  0.01928068 0.01873102 0.00882483 0.04558572]