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Python图像分割之均匀性度量法分析

均匀性度量图像分割是图像像素分割的一种方法，当然还有其他很多的方法。本文将主要介绍下其原理和实现代码，感兴趣的小伙伴可以学习一下

均匀性度量图像分割是图像像素分割的一种方法，当然还有其他很多的方法。这里简单的介绍下其原理和实现代码【有源码】

其流程大概分为一下几步

1、确定一个阈值

2、计算阈值两边的像素个数、占比、以及方差

3、将两边的方差和占比想乘再相加

4、循环1~3的步骤

下面以这个例子为示例做一个演示

计算公式：

阈值为: 1

阈值左边值为: [1, 1, 0, 0, 0] 均值: 0.08

阈值右边值为: [3, 9, 9, 8, 2, 3, 7, 3, 3, 6, 6, 4, 6, 8, 2, 5, 2, 9, 2, 6] 均值: 4.12

阈值左边方差为: 1.712

阈值右边方差为: 147.76800000000003

方差和比例相乘为: 118.55680000000002

阈值为: 2

阈值左边值为: [1, 2, 1, 0, 2, 0, 2, 2, 0] 均值: 0.4

阈值右边值为: [3, 9, 9, 8, 3, 7, 3, 3, 6, 6, 4, 6, 8, 5, 9, 6] 均值: 3.8000000000000007

阈值左边方差为: 11.440000000000003

阈值右边方差为: 150.04

方差和比例相乘为: 100.144

阈值为: 3

阈值左边值为: [1, 3, 2, 1, 3, 3, 3, 0, 2, 0, 2, 2, 0] 均值: 0.8799999999999999

阈值右边值为: [9, 9, 8, 7, 6, 6, 4, 6, 8, 5, 9, 6] 均值: 3.3200000000000003

阈值左边方差为: 25.347200000000004

阈值右边方差为: 186.14879999999997

方差和比例相乘为: 102.53196799999999

阈值为: 4

阈值左边值为: [1, 3, 2, 1, 3, 3, 3, 0, 4, 2, 0, 2, 2, 0] 均值: 1.0399999999999998

阈值右边值为: [9, 9, 8, 7, 6, 6, 6, 8, 5, 9, 6] 均值: 3.16

阈值左边方差为: 31.0624

阈值右边方差为: 199.56159999999997

方差和比例相乘为: 105.20204799999998

阈值为: 5

阈值左边值为: [1, 3, 2, 1, 3, 3, 3, 0, 4, 2, 0, 5, 2, 2, 0] 均值: 1.2399999999999998

阈值右边值为: [9, 9, 8, 7, 6, 6, 6, 8, 9, 6] 均值: 2.96

阈值左边方差为: 41.18400000000001

阈值右边方差为: 213.536

方差和比例相乘为: 110.12480000000001

阈值为: 6

阈值左边值为: [1, 3, 2, 1, 3, 3, 3, 6, 0, 6, 4, 6, 2, 0, 5, 2, 2, 6, 0] 均值: 2.1999999999999997

阈值右边值为: [9, 9, 8, 7, 8, 9] 均值: 2.0

阈值左边方差为: 88.96000000000002

阈值右边方差为: 244.0

方差和比例相乘为: 126.16960000000002

阈值为: 7

阈值左边值为: [1, 3, 2, 1, 3, 7, 3, 3, 6, 0, 6, 4, 6, 2, 0, 5, 2, 2, 6, 0] 均值: 2.4800000000000004

阈值右边值为: [9, 9, 8, 8, 9] 均值: 1.7200000000000002

阈值左边方差为: 103.488

阈值右边方差为: 237.87199999999996

方差和比例相乘为: 130.3648

阈值为: 8

阈值左边值为: [1, 3, 8, 2, 1, 3, 7, 3, 3, 6, 0, 6, 4, 6, 8, 2, 0, 5, 2, 2, 6, 0] 均值: 3.12

阈值右边值为: [9, 9, 9] 均值: 1.08

阈值左边方差为: 143.4368

阈值右边方差为: 188.17919999999998

方差和比例相乘为: 148.805888

2
100.144

结论：

最后我们发现以像素点为4的来分的时候，两边方差与占比的乘积最小，因此最佳阈值就是【2】

源码

import numpy as np

#

data = [1, 3, 9, 9, 8,

        2, 1, 3, 7, 3,

        3, 6, 0, 6, 4,

        6, 8, 2, 0, 5,

        2, 9, 2, 6, 0]

# data = [0, 1, 3, 1, 5,

#         7, 8, 9, 7]

max = np.max(data)

length = len(data)

num_min_data = []

num_max_data = []

arr_var = 0

min_result = 1000

result_threshold = 0

def myMean(arrs):

        resultss = 0.0

        data={}

        for i in arrs:

                data[i]= data.get(i,0)+1

        for i in data:

                resultss += i*(data[i]/length)

        return resultss

def fz(arrs):

        results = 0.0

        mean = myMean(arrs)

        for i in arrs:

                results+=(mean-i)**2

        return results

for i in range(1,max):

        num_min_data = []

        num_max_data = []

        for j in range(length):

                if data[j]>i:

                        num_max_data.append(data[j])

                else:

                        num_min_data.append(data[j])

        arr_var_max = fz(num_max_data)

        arr_var_min = fz(num_min_data)

        print("----------------------------------")

        print("阈值为:",i)

        print("阈值左边值为:",num_min_data,"均值:",myMean(num_min_data))

        print("阈值右边值为:",num_max_data,"  均值:",myMean(num_max_data))

        print("阈值左边方差为: ",arr_var_min)

        print("阈值右边方差为: ",arr_var_max)

        ratio_left   = arr_var_min*len(num_min_data) / length

        ratio_right  = arr_var_max*len(num_max_data) / length

        ratio_last = ratio_left+ratio_right

        print("方差和比例相乘为: ",ratio_last)

        if (ratio_last<min_result):

                min_result = ratio_last

                result_threshold = i

print("*"*50)

print(result_threshold)

print(min_result)

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到此这篇关于Python图像分割之均匀性度量法分析的文章就介绍到这了

原文链接：https://blog.csdn.net/jeekmary/article/details/121797981

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