Python中OTSU算法的原理与实现详解

import numpy as np
import matplotlib.pyplot as plt

# 计算类间方差
def getVar(img, th):
    slct = img>th
    p1 = np.sum(slct)/img.size
    if p1 in [0, 1]:
        return 0
    p2 = 1-p1
    m1 = np.mean(img[slct])
    m2 = np.mean(img[~slct])
    m = p1*m1+p2*m2
    return p1*m1**2+p2*m2**2-m**2

def otsu(img):
    vs = [getVar(img, th) for th in range(256)]
    return np.argmax(vs)

def drawOne(fig, index, img):
    fig.add_subplot(index)
    plt.imshow(img, cmap="gray")
    plt.axis('off')

def test():
    img = plt.imread(r"D:\Code\NotePL\python\lena.jpg").astype(float)
    img = np.mean(img, axis=2)
    th = otsu(img)
    print(th)
    fig = plt.figure(figsize=(8,3))
    drawOne(fig, 131, img)
    drawOne(fig, 132, img>th)
    drawOne(fig, 133, img<=th)
    plt.tight_layout()
    plt.show()

img = plt.imread(r"D:\Code\NotePL\python\lena.jpg").astype(float)
img = np.mean(img, axis=2)
vs = [getVar(img, th) for th in range(256)]
print(np.argmax(vs))    # 121
plt.plot(vs)
plt.show()

def climb(img, step, st=0, err=0.1):
    vSt = getVar(img, st)
    while abs(step)>err:
        ed = st+step
        vEd = getVar(img, ed)
        if vEd < vSt:
            step = -step/2
        st, vSt = ed, vEd
    return ed

climb(img, 40)
# 121.71875