OpenCV hand rubbing Otsu method binarization

 

import numpy as np
import cv2
lena=cv2.imread("C:\OpenCVAbout\RRR.jpg",0)#后面加0直接变黑白
h,c=lena.shape
N=h*c
from collections import Counter
arr = lena
counter = Counter()
for row in arr:
    counter.update(row)
#print(counter)#counter是新的字典
counter1={k:counter[k] for k in sorted(counter.keys())}
counter2={k:(counter[k]/N) for k in sorted(counter.keys())}#(counter[k]/N)是出现的概率
print(counter1)#次数直方图
print(counter2)#概率直方图


#n=0
#for i in sorted(counter2.keys()):
    #n=n+counter2[i]*i
#print(n)  # n是整张图像的加权平均值
#n=0
#for i in sorted(counter2.keys()):
   # n = n + counter2[i]
#print(n)#n是每一种像素的出现的总概率q
dd={}
for r in range(0,255):
    p0=0
    p1=0
    m0=0
    m1=0
    for i in sorted(counter2.keys()):
        if i<=r:
            p0=p0+counter2[i]
            m0=m0+counter2[i]*i
        else:
            p1=p1+counter2[i]
            m1 = m1 + counter2[i] * i
    s=p0*p1*(m0-m1)*(m0-m1)#计算求出评分值
    dd[r]=s
print(dd)#成功创建包含每一个阈值评分的字典
x=0
j=0
for i in range(0,255):
    if dd[i] > x :
        x=dd[i]
        j=i
print(x)
print(j)
ret1,dst1=cv2.threshold(lena,j,255,cv2.THRESH_BINARY)
cv2.imshow("0",lena)
cv2.imshow("1",dst1)
kernal=np.ones((10,10),np.uint8)
erosion=cv2.erode(dst1,kernal)
cv2.imshow("ero",erosion)

cv2.waitKey(0)
cv2.destroyAllWindows()