基于阈值的图像处理

学习目标：图像处理课程作业，基于阈值处理图片。

原图：

学习产出：

import cv2 as cv
import numpy as np
#T分割照片为G1和G2，并计算其平均值
def threshold(img,T):
    rows = img.shape[0]
    columns = img.shape[1]
    G1=G2=0
    g1=g2=0
    for i in range(rows):
        for j in range(columns):
            if img[i,j]>T:
                G1+=img[i,j]
                g1+=1
            else:
                G2+=img[i,j]
                g2+=1
    m1=int(G1/g1)
    m2=int(G2/g2)   # m1，m2计算两组像素均值
    T0=int((m1+m2)/2)   # 据公式计算新的阈值
    return T0

def choose(img,T):
    rows = img.shape[0]
    columns = img.shape[1]
    img1=np.zeros((rows,columns),np.uint8)
    T0=T
    T1=threshold(img,T0)
    for k in range (100):   # 迭代次数为经验值，可据实际情况选定
        if abs(T1-T0) < 3:   # 若新阈值减旧阈值差值小于三，则为二值图最佳阈值
            for i in range(rows):
                for j in range(columns):
                    if img[i,j]>T1:
                        img1[i,j]=255
                    else:
                        img1[i,j]=0
            break
        else:
            T2=threshold(img,T1)
            #变量转换，保证if条件为新阈值减旧阈值
            T0=T1
            T1=T2
    return img1

image=cv.imread('word.jpg',cv.IMREAD_GRAYSCALE)
threshold_image=choose(image,115)
cv.imshow("threshold_image",threshold_image)
cv.waitKey(0)
cv.destroyAllWindows()

结果：