利用该文献中的三个计算方法分别利用python编写相应的程序

针对上述文献中描述的三种阈值化算法（Algorithm 1, Algorithm 2 和 Algorithm 3），以下是基于Python语言实现这些算法的基本框架示例：

### Algorithm 1 实现

import numpy as np

def entropy_based_threshold(hist):
    hist = hist / np.sum(hist)
    cumulative_sum = np.cumsum(hist)
    
    def eval_function(s):
        p1 = cumulative_sum[s]
        p2 = 1 - p1
        
        if p1 == 0 or p2 == 0:
            return float('-inf')
        
        max_p1 = np.max(hist[:s+1])
        max_p2 = np.max(hist[s+1:])
        
        entropy_p1 = -np.sum(hist[:s+1][hist[:s+1]>0] * np.log2(hist[:s+1][hist[:s+1]>0]))
        entropy_p2 = -np.sum(hist[s+1:][hist[s+1:]>0] * np.log2(hist[s+1:][hist[s+1:]>0]))
        
        return entropy_p1 + entropy_p2 + np.log2(max_p1) + np.log2(max_p2)

    thresholds = range(len(hist))
    scores = [eval_function(t) for t in thresholds]

    optimal_threshold = thresholds[np.argmax(scores)]
    return optimal_threshold

### Algorithm 2 实现

def anisotropy_coefficient(hist):
    cumsum_hist = np.cumsum(hist)
    m = np.where(cumsum_hist >= 0.5*np.sum(hist))[0][0]
    
    entropy_part = -np.sum(hist[:m+1] * np.log2(hist[:m+1])) if np.any(hist[:m+1]) else 0
    
    alpha = entropy_part / np.sum(hist)
    return m, alpha

def threshold_anisotropy(hist):
    m, alpha = anisotropy_coefficient(hist)
    if alpha < 0.5:
        threshold = m
    else:
        threshold = m + 1
    return threshold

### Algorithm 3 实现

def new_entropy_based_threshold(hist):
    hist = hist / np.sum(hist)
    cumulative_sum = np.cumsum(hist)
    
    def q_func(s):
        p1 = cumulative_sum[s]
        p2 = 1 - p1
        
        if p1 == 0 or p2 == 0:
            return float('-inf')

        entropy_p1 = -np.sum(hist[:s+1][hist[:s+1]>0] * np.log2(hist[:s+1][hist[:s+1]>0]))
        entropy_p2 = -np.sum(hist[s+1:][hist[s+1:]>0] * np.log2(hist[s+1:][hist[s+1:]>0]))

        return np.log2(p1) + np.log2(p2) + entropy_p1 + entropy_p2

    thresholds = range(len(hist))
    scores = [q_func(t) for t in thresholds]

    optimal_threshold = thresholds[np.argmax(scores)]
    return optimal_threshold

请注意，以上代码仅提供了一个基础的框架来执行各个算法，并没有考虑所有可能的情况和边界条件处理。在实际应用时需要进一步优化和完善。同时，输入`hist`应该是一个表示灰度级别的直方图数组。