python+opencv閾值

在opencv中提供了閾值操作，給定閾值進行分割圖像

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的反轉
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
#截斷值 >127---變為等於255
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
#大於127不變，小於等於127變為0---to zero
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
#thresh4的反轉
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()

上圖為讀灰度圖得到的結果，下圖為讀原圖得到的結果。

 

 

 

 上面給了五種方式，上圖第一個為原圖。改變 cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)的參數可得到不同的結果

1、cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)：像素值大於127的值變為255，小於127的變為0。

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

3、ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)：像數值大於127變為255，小於127變為不變。

4、ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)：像數值大於127不變，小於127變為0。

5、ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)：與4相反，像數值大於127變為0，小於127不變。