commit d1b3a309591ad8f92781e0c7224b76f273c6f2ab
Author: wangjin0928 <wangjin0928@163.com>
Date:   Mon Jul 17 17:27:57 2023 +0800

    v1.0

diff --git a/0_mindist_between_two_array.py b/0_mindist_between_two_array.py
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+++ b/0_mindist_between_two_array.py
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+# -*- coding: UTF-8 -*-
+import cv2
+import time
+import numpy as np
+import skimage.exposure
+'''
+两个区域间最短距离
+https://www.cnpython.com/qa/1329750
+'''
+
+import math
+
+
+def downsample(num_arr,downsample_rate):
+    '''
+    下采样数组，隔着downsample_rate个数取一个值
+    num_arr为数组
+    downsample_rate为采用概率，为1-n的正整数
+    '''
+    num_arr_temp=[]
+    for i in range(len(num_arr)//downsample_rate-1):
+        num_arr_temp.append(num_arr[i*downsample_rate])
+    return num_arr_temp
+
+
+def array_distance(arr1,arr2):
+    '''
+    计算两个数组中，每任意两个点之间L2距离
+    arr1和arr2都必须是numpy数组
+    且维度分别是mx2，nx2
+    输出数组维度为mxn
+    '''
+    m,_=arr1.shape
+    n,_=arr2.shape
+    arr1_power = np.power(arr1, 2)
+    xxx=arr1_power[:, 0]
+    arr1_power_sum = arr1_power[:, 0] + arr1_power[:, 1]   #第1区域，x与y的平方和
+    yyy=arr1_power_sum
+    arr1_power_sum = np.tile(arr1_power_sum, (n, 1))       #将arr1_power_sum沿着y轴复制n倍，沿着x轴复制1倍，这里用于与arr2进行计算。 nxm 维度
+    zzz=arr1_power_sum
+    arr1_power_sum = arr1_power_sum.T                      #将arr1_power_sum进行转置
+    arr2_power = np.power(arr2, 2)
+    arr2_power_sum = arr2_power[:, 0] + arr2_power[:, 1]       #第2区域，x与y的平方和
+    arr2_power_sum = np.tile(arr2_power_sum, (m, 1))           #将arr1_power_sum沿着y轴复制m倍，沿着x轴复制1倍，这里用于与arr1进行计算。   mxn 维度
+    dis = arr1_power_sum + arr2_power_sum - (2 * np.dot(arr1, arr2.T))   #np.dot(arr1, arr2.T)矩阵相乘，得到xy的值。
+    dis = np.sqrt(dis)
+    return dis
+
+# 中间输入的代码
+# 将数组存在num_arr1和num_arr2中
+
+t1=time.time()
+# 1.读入图片
+# img = cv2.imread('demo/171.png')
+img = cv2.imread('demo/9.png')
+t2=time.time()
+img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+contours, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+# 2.寻找轮廓(多边界)
+contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2)
+
+
+# 3.轮廓数组转为列表(多边界)
+list_contours=[]
+record=[]
+num_arr1=contours[0]
+num_arr2=contours[1]
+
+
+ssss1=np.squeeze(num_arr1, 1)
+ssss2=np.squeeze(num_arr2, 1)
+
+
+# 3.对边界进行下采样，减小点数量。
+num_arr11=downsample(num_arr1,10)   #下采样边界点
+num_arr22=downsample(num_arr2,10)   #下采样边界点
+
+
+
+
+print(num_arr1)
+t3=time.time()
+
+dist_arr=array_distance(ssss1,ssss2)
+min_dist=dist_arr[dist_arr>0].min()
+
+print(min_dist)
+
+
+
+
+# print('两区域最小距离',min(record))
+t4=time.time()
+
+print('读图时间:%s 找边界时间:%s 区域最短距离计算时间:%s'%(t2-t1,t3-t2,t4-t3))
+
diff --git a/demo/9.jpg b/demo/9.jpg
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diff --git a/demo/9.png b/demo/9.png
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diff --git a/readme.txt b/readme.txt
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+++ b/readme.txt
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+1、0_mindist_between_two_array.py
+两个区域之间最小距离（采用数组计算）
+