Mahotas – 图像的完整直方图

在本文中，我们将了解如何在 mahotas 中绘制图像的直方图。图像直方图是一种直方图，用作数字图像中色调分布的图形表示。它绘制每个色调值的像素数。通过查看特定图像的直方图，观众将能够一眼判断整个色调分布。
在本教程中，我们将使用“lena”图像，下面是加载它的命令。

mahotas.demos.load('lena')

下面是莉娜的图片

In order to do this we will use mahotas.fullhistogram method
Syntax : mahotas.fullhistogram(img)
Argument : It takes image object as argument
Return : It returns ndarray of type np.uint32 
 

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 histogram
value = mahotas.fullhistogram(img)
  
# showing histograph
plt.hist(value)

输出 ：

Image

(array([82., 50., 34., 21., 24., 16.,  9.,  6.,  9.,  5.]),
 array([   0.,  391.9,  783.8, 1175.7, 1567.6, 1959.5, 2351.4, 2743.3,
        3135.2, 3527.1, 3919. ]),
 a list of 10 Patch objects)

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 histogram
value = mahotas.fullhistogram(img)
  
# showing histograph
plt.hist(value)

输出 ：

Image

(array([27., 29., 56., 20., 23., 41., 21., 23., 10.,  6.]),
 array([1.0000e+00, 4.4780e+02, 8.9460e+02, 1.3414e+03, 1.7882e+03,
        2.2350e+03, 2.6818e+03, 3.1286e+03, 3.5754e+03, 4.0222e+03,
        4.4690e+03]),
 a list of 10 Patch objects>