OTSU二值化俗称大津法，主要用于图像的阈值分割，主要是针对双峰影像（指的是图像的直方图为双峰影像），简单来说OTSU算法就是要找到一个阈值（t)，是的同一类加权方差最小，需要满足下列关系式
【OPencv中的OTSU二值化】
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简单来说就是在两个峰之间找到一个阈值t,将这两个峰分开，并且使每一个峰内的方差最小，实现这个算法的python代码如下
import cv2
import numpy as np
img = cv2.imread('noisy2.png',0)
blur = cv2.GaussianBlur(img,(5,5),0)
# find normalized_histogram, and its cumulative distribution function
# 计算归一化直方图
#CalcHist(image, accumulate=0, mask=NULL)
hist = cv2.calcHist([blur],[0],None,[256],[0,256])
hist_norm = hist.ravel()/hist.max()
Q = hist_norm.cumsum()
bins = np.arange(256)
fn_min = np.inf
thresh = -1
for i in xrange(1,256):
p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
b1,b2 = np.hsplit(bins,[i]) # weights
# finding means and variances
m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2
v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2
# calculates the minimization function
fn = v1*q1 + v2*q2
if fn < fn_min:
fn_min = fn
thresh = i
# find otsu's threshold value with OpenCV function
ret, otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print thresh,ret推荐阅读
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