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  • 迭代的(最优的)阈值选择

    迭代法阈值选择算法是对双峰法的改进,他首先选择一个近似的阈值T,将图像分割成两个部分,R1和R2,计算出区域R1和R2的均值u1和u2,再选择新的

    阈值T=(u1+u2)/2;

    重复上面的过程,直到u1和u2不在变化为止,

    实现前后对比:

    实现代码:

    import numpy as np
    import cv2
    
    img = cv2.imread('./0.jpg', 0)
    #print(img[2, 4])
    max_value = np.max(img)
    min_value = np.min(img)
    average_value = (max_value+min_value)/2
    
    h, w = img.shape
    
    while True:
        fnum = 0
        bnum = 0
        fvalue = 0
        bvalue = 0
        faverage = 0
        baverage = 0
        b = 0
        
        for i in range(h):
            for j in range(w):
                if img[i, j] > average_value:
                    fnum += 1
                    fvalue += img[i, j]
                else:
                    bnum += 1
                    bvalue += img[i, j]
                    
        faverage = fvalue/fnum
        baverage = bvalue/bnum
        new_average = int((faverage + baverage)/2)
        if new_average == int(average_value):
            break
        else:
            average_value = new_average
            print(new_average)
    
    img = cv2.threshold(img, new_average, 255, cv2.THRESH_BINARY)[1]
    cv2.imshow('img', img)
    cv2.imwrite('9.jpg', img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
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  • 原文地址:https://www.cnblogs.com/czz0508/p/11059199.html
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