原理
Otsu 的原理是最小化類間方差，從像素分布直方圖的角度來看就是在像素分布直方圖的峰值之間找一個點，使得兩個類之間的方差最大，類內(nèi)方差最小。
Screen Shot 2019-01-28 at 12.02.02 PM.png
image.png
這個算法可以用于OCR, 車牌分割等等
實現(xiàn)
- 這個實現(xiàn)是遍歷每一個分割值，然后求解全局最優(yōu)解，具體到工程應(yīng)用也有一些優(yōu)化算法。
img = cv.imread('noisy2.png',0)
blur = cv.GaussianBlur(img,(5,5),0)
# find normalized_histogram, and its cumulative distribution function
hist = cv.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 range(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 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
print( "{} {}".format(thresh,ret) )
