如何获取二值化图像后的最大类间方差,用python代码实现
时间: 2024-05-07 10:16:52 浏览: 10
您好,针对您的问题,可以通过Otsu算法来获取二值化图像后的最大类间方差。以下是用python实现的代码:
1. 导入必要的库:
```python
import numpy as np
import cv2
from matplotlib import pyplot as plt
```
2. 加载图片并进行灰度化:
```python
img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
```
3. 计算图像的Otsu阈值:
```python
# Otsu's thresholding
ret2,th2 = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
```
4. 统计图像的像素值和像素数,计算图像的类间方差:
```python
# calculate the histogram of the image
hist,bins = np.histogram(gray.ravel(),256,[0,256])
# calculate the probability mass function (pmf) of the image
pmf = hist/(img.shape[0]*img.shape[1])
# calculate the cumulative distribution function (cdf) of the pmf
cdf = pmf.cumsum()
# calculate the mean value of the image
mean = np.arange(0,256)*pmf
mean = mean.cumsum()
# calculate the variance between classes of the image
variance = ((mean[-1]*cdf - mean*cdf)**2) / (cdf*(1 - cdf))
```
5. 获取最大类间方差对应的二值化阈值:
```python
# get the threshold corresponding to the maximum variance
idx = np.argmax(variance)
thresh = bins[idx+1]
```
6. 输出结果:
```python
print('The Otsu threshold is: ', ret2)
print('The threshold obtained by maximizing inter-class variance is: ', thresh)
```
希望以上代码能够对您有所帮助。如果您还有其他问题,请继续提问。