GLCM is symmetrical, therefore Sum Average = 2 * Joint Average, only 1 needs to be calculated什么意思
时间: 2024-02-26 16:51:48 浏览: 18
这句话的意思是,GLCM(灰度共生矩阵)是对称矩阵,因此矩阵的总平均值等于两个灰度共生矩阵元素的平均值之和。因此,只需要计算其中一个平均值即可。也就是说,如果你已经计算了联合平均值(Joint Average),则可以用它来计算总平均值(Sum Average),反之亦然。这个性质可以用来减少计算量和提高计算效率。
相关问题
df = pd.DataFrame(columns=['0','90','180','270']) contrast = graycoprops(glcm, 'contrast') dissimilarity = graycoprops(glcm, 'dissimilarity') homogeneity = graycoprops(glcm, 'homogeneity') energy = graycoprops(glcm, 'energy') correlation = graycoprops(glcm, 'correlation') asm = graycoprops(glcm, 'ASM') 将下面的六个1*4的矩阵赋值给df的每一行,每一行允许自定义索引
您可以使用 `loc` 方法将这些矩阵赋值给 DataFrame 的每一行,并自定义索引。下面是一个示例:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(columns=['0', '90', '180', '270'])
contrast = np.array([1, 2, 3, 4])
dissimilarity = np.array([5, 6, 7, 8])
homogeneity = np.array([9, 10, 11, 12])
energy = np.array([13, 14, 15, 16])
correlation = np.array([17, 18, 19, 20])
asm = np.array([21, 22, 23, 24])
df.loc[0] = contrast
df.loc[1] = dissimilarity
df.loc[2] = homogeneity
df.loc[3] = energy
df.loc[4] = correlation
df.loc[5] = asm
df.index = ['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'asm']
print(df)
```
这将输出:
```
0 90 180 270
contrast 1 2 3 4
dissimilarity 5 6 7 8
homogeneity 9 10 11 12
energy 13 14 15 16
correlation 17 18 19 20
asm 21 22 23 24
```
在上述示例中,首先创建一个空的 DataFrame,并定义了索引列。然后,使用 `loc` 方法将每个矩阵赋值给 DataFrame 的每一行,并使用自定义的索引值。最后,使用 `index` 属性将索引列设置为自定义的索引。
p[i][j] = glcm[i][j] / np.sum(glcm[i][j])
This line of code normalizes the values in a gray-level co-occurrence matrix (GLCM) by dividing each element by the sum of all elements in the matrix. GLCM is a statistical method used to analyze the spatial relationship between pairs of pixels in an image. Each element in the matrix represents the frequency of occurrence of a pair of pixel values at a given distance and direction within an image. Normalization is necessary to ensure that the sum of all elements in the matrix is equal to one, which makes it easier to compare GLCMs of different images. The resulting matrix, p, represents the probability distribution of pairs of pixel values in the image.