Mahotas 简明教程
Mahotas - Threshold Adjacency Statistics
阈值邻接统计 (TAS) 是一种用于从图像中提取任何重要信息的技术。在了解 TAS 如何工作之前，让我们简要了解阈值化。
Threshold adjacency statistics (TAS) is a technique used to extract any important information from an image. Before understanding how TAS works, let us understand thresholding in brief.
阈值化是一种技术，它根据特定值（阈值）将图像分为前景区域和背景区域。前景区域由其强度值大于阈值像素构成。
Thresholding is a technique that separates an image into a foreground region and a background region based on a specific value (threshold value). The foreground region consists of pixels whose intensity value is greater than the threshold value.
另一方面，背景区域由其强度值小于阈值的像素构成。
On the other hand, the background region consists of pixels whose intensity value is less than the threshold value.
TAS 通过计算其强度值超过阈值的像素数量来工作。此外，它还考虑了一定数量的其强度值也超过阈值的相邻像素。
TAS work by counting the number of pixels whose intensity value exceeds the threshold value. Additionally, it considers a specified number of neighboring pixels whose intensity value also exceeds the threshold value.
Threshold Adjacency Statistics in Mahotas
在 Mahotas 中，我们可以使用 mahotas.tas() 和 mahotas.pftas() 函数来计算图像的阈值邻接统计信息。然后可以使用计算的统计信息来定位和从图像中提取重要信息。
In Mahotas, we can use the mahotas.tas() and mahotas.pftas() functions to calculate the threshold adjacency statistics of an image. The calculated statistics can then be used to locate and extract important information from the image.
tas() 函数和 pftas() 函数之间的唯一区别在于，在 pftas() 函数中，我们可以设置用于计算 TAS 的任何阈值。
The only difference between the tas() function and the pftas() function is that in pftas() function, we can set any threshold value that is used to calculate TAS.
相反，tas() 函数不使用阈值来计算 TAS。
In contrast, the tas() function does not use a threshold value to calculate TAS.
The mahotas.tas() function
mahotas.tas() 函数将图像作为输入，并返回一个包含阈值邻接统计信息的列表。
The mahotas.tas() function takes an image as input and returns a list having the threshold adjacency statistics.
以下是 tas() 函数在 mahotas 中的基本语法 -
Following is the basic syntax of the tas() function in mahotas −
mahotas.features.tas(img)其中，
where,
-
img − It is the input image.
在下面提到的示例中，我们正在使用 mh.tas() 函数计算图像的 TAS 值。
In the example mentioned below, we are calculating the TAS values of an image using the mh.tas() function.
import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread('sun.png')
# Computing TAS
tas = mh.features.tas(image)
# Printing the TAS value
print(tas)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 1)
# Displaying the original image
axes.imshow(image, cmap='gray')
axes.set_title('Original Image')
axes.set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()上述代码的输出如下：
Output of the above code is as follows −
[8.37835351e-01 1.15467657e-02 1.39075269e-02 9.92426122e-03
1.03643093e-02 6.76089647e-03 1.09572672e-02 6.88336269e-03
8.17548510e-03 6.01115411e-02 6.08145111e-03 5.10483489e-03
4.16108390e-03 2.81568522e-03 1.77506830e-03 1.46786490e-03
6.81867008e-04 6.12677053e-04 2.44932441e-04 2.76759821e-04
. . .
4.27349413e-03 7.01932689e-03 4.50541370e-03 5.45604649e-03
6.41356563e-02 4.43892481e-03 4.80936290e-03 4.46979465e-03
3.91413752e-03 2.33898410e-03 3.27299467e-03 1.12872803e-03
2.06353013e-03 4.92334385e-04 1.22371215e-03 1.14772485e-04
6.03149199e-04 3.32444440e-05 3.26112165e-04 1.18730157e-05
1.28228570e-04 0.00000000e+00]我们得到下方的图像作为输出−
We get the below image as output −
The mahotas.pftas() function
mahotas.pftas() 函数将图像和阈值作为输入。它返回一个具有阈值邻接统计信息的列表。
The mahotas.pftas() function takes an image and a threshold value as inputs. It returns a list having the threshold adjacency statistics.
下面是 mahotas 中 pftas() 函数的基本语法−
Following is the basic syntax of the pftas() function in mahotas −
mahotas.features.pftas(img, T={mahotas.threshold.otsu(img)})其中，
where,
-
img − It is the input image.
-
T (optional) − It defines the threshold algorithm used in TAS (by default it uses Otsu’s method).
在下面的示例中，我们使用 mh.pftas() 函数计算图像的 TAS。
In the following example, we are computing the TAS of an image using the mh.pftas() function.
import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread('nature.jpeg')
# Converting it to grayscale
image = mh.colors.rgb2gray(image).astype(np.uint8)
# Computing parameter free TAS
pftas = mh.features.pftas(image)
# Printing the parameter free TAS value
print(pftas)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 1)
# Displaying the original image
axes.imshow(image, cmap='gray')
axes.set_title('Original Image')
axes.set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()以下是上面代码的输出： -
Following is the output of the above code −
[9.57767091e-01 1.48210628e-02 8.58153775e-03 1.18217967e-02
3.89970314e-03 1.86659948e-03 7.82131473e-04 3.19863291e-04
1.40214046e-04 9.73817262e-01 1.23385295e-02 5.89271152e-03
4.39412383e-03 1.90987201e-03 8.34387151e-04 4.60922081e-04
2.31642892e-04 1.20548852e-04 9.77691695e-01 8.29460231e-03
3.91949031e-03 7.21369229e-03 1.68522833e-03 7.53014919e-04
3.10737802e-04 1.12475646e-04 1.90636688e-05 9.47186804e-01
1.14563743e-02 9.65510102e-03 1.76918166e-02 5.35205921e-03
3.38515157e-03 2.13944340e-03 1.88754119e-03 1.24570817e-03
9.80623501e-01 3.72244140e-03 2.75392589e-03 4.22681210e-03
2.28359248e-03 1.92155953e-03 1.72971300e-03 1.63378974e-03
1.10466466e-03 9.59139669e-01 7.94832237e-03 7.15439233e-03
1.68349257e-02 3.75312384e-03 1.74123294e-03 9.83390623e-04
1.06007705e-03 1.38486661e-03]获得的图像如下所示：
The image obtained is as follows −
Using Mean Threshold Value
我们还可以使用平均阈值计算图像的阈值邻接统计信息。平均阈值是指通过取图像中平均像素强度值计算出的阈值。
We can also calculate the threshold adjacency statistics of an image using the mean threshold value. Mean threshold value refers to a threshold value calculated by taking the average pixel intensity value of an image.
简单来说，阈值是通过将图像中所有像素的强度值相加，然后将该和除以图像中的像素总数来计算的。
In simple terms, the threshold value is calculated by adding up the intensity values of all the pixels in an image and then dividing that sum by the total number of pixels in the image.
在 mahotas 中，我们首先使用 mean() 函数计算平均阈值。然后，我们在 pftas() 函数的 T 参数中设置此值，以使用平均阈值计算 TAS。
In mahotas, we first calculate the mean threshold value using the mean() function. Then, we set this value in the T parameter of the pftas() function to calculate TAS using mean threshold value.
Example
在这里，我们使用平均阈值获取图像的 TAS。
In here, we are getting the TAS of an image using the mean threshold value.
import mahotas as mh
import numpy as np
import matplotlib.pyplot as mtplt
# Loading the images
image = mh.imread('tree.tiff')
# Converting it to grayscale
image = mh.colors.rgb2gray(image).astype(np.uint8)
# Calculating threshold value
threshold = image > np.mean(image)
# Computing parameter free TAS using mean threshold
pftas = mh.features.pftas(image, threshold)
# Printing the parameter free TAS value
print(pftas)
# Creating a figure and axes for subplots
fig, axes = mtplt.subplots(1, 1)
# Displaying the original image
axes.imshow(image, cmap='gray')
axes.set_title('Original Image')
axes.set_axis_off()
# Adjusting spacing between subplots
mtplt.tight_layout()
# Showing the figures
mtplt.show()执行上面的代码后，我们得到以下输出： -
After executing the above code, we get the following output −
[0.63528106 0.07587514 0.06969174 0.07046435 0.05301355 0.0396411
0.0278772 0.0187047 0.00945114 0.51355051 0.10530301 0.0960256
0.08990634 0.06852526 0.05097649 0.03778379 0.02519265 0.01273634
0.69524747 0.0985423 0.07691423 0.05862548 0.03432296 0.01936853
0.01058033 0.00482901 0.00156968 0.46277808 0.17663377 0.13243407
0.10085554 0.06345864 0.03523172 0.01735837 0.00835911 0.00289069
0.78372479 0.0746143 0.04885744 0.03739208 0.02555628 0.01563048
0.00822543 0.00436208 0.00163713 0.70661663 0.07079426 0.05897885
0.06033083 0.04280415 0.02972053 0.01632203 0.01043743 0.00399529]输出图像如下−
The output image is as follows −
