简单阈值，自适应阈值，Otsu's二值化等图像阈值处理方法。

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

img_path = 'C:/Users/xpp/Desktop/lena.jpg'


img = cv2.imread(img_path)

img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)


ret,th1 = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY)
ret,th2 = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY_INV)
ret,th3 = cv2.threshold(img_gray,127,255,cv2.THRESH_TRUNC)
ret,th4 = cv2.threshold(img_gray,127,255,cv2.THRESH_TOZERO)
ret,th5 = cv2.threshold(img_gray,127,255,cv2.THRESH_TOZERO_INV)

titles = ['oringnal','gray_img','BINGARY','BINGARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img,img_gray,th1,th2,th3,th4,th5]
i = 0
for i in range(7):
   plt.subplot(3,3,i+1)
   plt.imshow(images[i],'gray')
   plt.title(titles[i])
   plt.xticks([]),plt.yticks([])

plt.show()

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



img1 =cv2.imread('C:/Users/xpp/Desktop/lena.jpg',0)

img = cv2.medianBlur(img1,5)

ret, th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,
                          cv2.THRESH_BINARY,11,2)
th3 =cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,
                          cv2.THRESH_BINARY,11,5)



th4 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

th5 =cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,5)

titles = [
  'origianl_ images','means-image','gloabal(v = 127)','Mean(C =2)',
'Mean(C =5)','Gayssian(C = 2)',
'Gayssian(C = 5)'
]


images =[img1,img,th1,th2,th3,th4,th5]

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

plt.show()

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



img1 =cv2.imread('C:/Users/xpp/Desktop/lena.jpg',0)


ret1,th1 = cv2.threshold(img1,127,255,cv2.THRESH_BINARY)


ret2,th2 = cv2.threshold(img1,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)



blur =cv2.GaussianBlur(img1,(5,5),0)

ret3,th3 =cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

## plot all the images and their histogram
images =[img1,0,th1,
        img1,0,th2,
        blur,0,th3]
titles =[

  'Ori','Hist','Glo(v =127)',
  'Ori', 'Hist', 'Ostu',

  'Gass', 'Hist', 'Ostu',

]


for i in range(3):
  plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
  plt.title(titles[i*3]),plt.xticks([]),plt.yticks([])
  plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
  plt.title(titles[i*3+1]),plt.xticks([]),plt.yticks([])
  plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
  plt.title(titles[i*3+2]),plt.xticks([]),plt.yticks([])
plt.show()