teste4.py

import cv2
import numpy as np
 
img = cv2.imread('data/frames/frames0/000235.jpg',0)
#blur = cv2.GaussianBlur(img,(5,5),0)
 
# find normalized_histogram, and its cumulative distribution functio
hist = cv2.calcHist([img],[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 range(1,256):
    p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
    q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
    if q1 == 0:
        q1 = 0.00000001
    if q2 == 0:
        q2 = 0.00000001
        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(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print (thresh)
print(ret)