在opencv中提供了阈值操作，给定阈值进行分割图像

import cv2
import numpy as np 
import matplotlib.pyplot as plt

img_gray=cv2.imread("c:\\Users\\Administrator\\Desktop\\123\\cat2.jpg",cv2.IMREAD_GRAYSCALE)

#>127--->255    <127----->0
ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
#thresh1的反转
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
#截断值 >127---变为等于255
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
#大于127不变，小于等于127变为0---to zero
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
#thresh4的反转
ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['original', 'binary','binaty_inv','trunc','tozero','tozero_inv']
images = [img_cat2,thresh1,thresh2,thresh3,thresh4,thresh5]

for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

上图为读灰度图得到的结果，下图为读原图得到的结果。

 

 

 

 上面给了五种方式，上图第一个为原图。改变 cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)的参数可得到不同的结果

1、cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)：像素值大于127的值变为255，小于127的变为0。

2、cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)：与1正好相反。

3、ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)：像数值大于127变为255，小于127变为不变。

4、ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)：像数值大于127不变，小于127变为0。

5、ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)：与4相反，像数值大于127变为0，小于127不变。