Mahotas – 计算线性二进制模式

在本文中，我们将了解如何在 mahotas 中获得图像的线性二进制模式。局部二进制模式是一种用于计算机视觉分类的视觉描述符。 LBP 是 1990 年提出的纹理光谱模型的特例。LBP 于 1994 年首次描述。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

mahotas.demos.nuclear_image()

下面是nuclear_image

为此，我们将使用 mahotas.features.lbp 方法

Syntax : mahotas.features.lbp(image, radius, points)
Argument : It takes image object and two integers as argument
Return : It returns 1-D numpy ndarray i.e histogram feature 
 

注意： this 的输入应该是过滤后的图像或加载为灰色
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]

示例 1：

Python3

# importing various libraries
import mahotas
import mahotas.demos
import mahotas as mh
import numpy as np
from pylab import imshow, show
import matplotlib.pyplot as plt
 
# loading nuclear image
nuclear = mahotas.demos.nuclear_image()
 
# filtering image
nuclear = nuclear[:, :, 0]
 
# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)
 
# setting threshold
threshed = (nuclear > nuclear.mean())
 
# making is labelled image
labeled, n = mahotas.label(threshed)
 
# showing image
print("Labelled Image")
imshow(labelled)
show()
 
# Computing Linear Binary Patterns
value = mahotas.features.lbp(labelled, 200, 5)
 
# showing histograph
plt.hist(value)

输出 ：

示例 2：

Python3

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
import matplotlib.pyplot as plt
  
# 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())
  
# making is labelled image
labeled, n = mahotas.label(gaussian)
 
# showing image
print("Labelled Image")
imshow(labelled)
show()
 
 
# Computing Linear Binary Patterns
value = mahotas.features.lbp(labelled, 200, 5, ignore_zeros = False)
 
 
# showing histograph
plt.hist(value)

输出 ：