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left"><a href="/2023/09/21/AI%E5%BA%94%E7%94%A8%E5%88%86%E4%BA%AB/" title="AI应用分享"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/AI.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="AI应用分享"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/09/21/AI%E5%BA%94%E7%94%A8%E5%88%86%E4%BA%AB/" title="AI应用分享">AI应用分享</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-09-21T12:00:00.000Z" title="发表于 2023-09-21 20:00:00">2023-09-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-09-21T12:30:00.000Z" title="更新于 2023-09-21 20:30:00">2023-09-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/">实战项目</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/">深度学习</a></span></div><div class="content">AI应用分享写在前面本文件用于23年9月22日和物联网的小伙伴们分享AI的应用，所用代码已开源：
个人博客：www.lizhiyang.xyz
CSDN博客：爱睡觉的咋 https://blog.csdn.net/weixin_63866037?spm=1000.2115.3001.5343
Opencv+PyQT+串口通信：https://pan.baidu.com/s/1Kz7HesfcM-LfQCJdFOeTVA?pwd=k6z7 提取码：k6z7
Yolov5教程：
Yolov5：强大到你难以想象──新冠疫情下的口罩检测:http://t.csdn.cn/Iutzk
一文详解Yolov5——基于Yolov5的火灾检测系统:http://t.csdn.cn/3N2Rx
Yolov5+图像分割+百度AI接口——车牌实时检测识别系统:http://t.csdn.cn/PVoeD
Yolov5移植树莓派实现目标检测:http://t.csdn.cn/rPyZc
Yolov5移植树莓派4B问题总结:http://t.csdn.cn/NfxWF
Opencv教程：https: ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2023/08/22/44-%E9%A1%BA%E5%BA%8F%E8%A1%A8/" title="1.顺序表"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/25.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="1.顺序表"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/08/22/44-%E9%A1%BA%E5%BA%8F%E8%A1%A8/" title="1.顺序表">1.顺序表</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-08-21T16:00:01.000Z" title="发表于 2023-08-22 00:00:01">2023-08-22</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-08-21T16:00:01.000Z" title="更新于 2023-08-22 00:00:01">2023-08-22</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E5%92%8C%E7%AE%97%E6%B3%95/">数据结构和算法</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84/">数据结构</a></span></div><div class="content">
概况在程序中，经常需要将一组（通常是同为某个类型的）数据元素作为整体管理和使用，需要创建这种元素组，用变量记录它们，传进传出函数等等。一组数据中包含的元素个数可能发生变化（可以增加或删除元素）。对于这种需求，最简单的解决办法是将这样一组元素看成一个序列，用元素在序列里的位置和顺序，表示实际应用中的某种有意义的信息，或者表示数据之间的某种关系。这样的一组序列元素的组织形式，我们可以将其抽象为线性表，一个线性表是某类元素的一个集合，还记录着元素之间的一种顺序关系。线性表是最基本的数据结构之一，在实际程序中应用非常广泛，它还经常被用作更复杂的数据结构的实现基础。根据线性表的实际存储方式，分为两种实现模型：
顺序表，将元素顺序地存放在一块连续的存储区里，元素间的顺序关系由它们的存储顺序自然表示。
链表，将元素存放在通过连接构造起来的一系列存储块中。
Python里面中的list和tuple两种类型采用了顺序表的实现技术，元组是不可变类型，因此不支持改变其内部状态的任何操作，其他方面元组与列表的性质相似。顺序表的操作
顺序表的操作增加
在尾端加入元素，时间复杂度是O(1)
非保序的加入 ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2023/08/21/42-Scharr%E7%AE%97%E5%AD%90/" title="25.Scharr算子"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/4.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="25.Scharr算子"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/08/21/42-Scharr%E7%AE%97%E5%AD%90/" title="25.Scharr算子">25.Scharr算子</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-08-20T16:00:25.000Z" title="发表于 2023-08-21 00:00:25">2023-08-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-08-20T16:00:25.000Z" title="更新于 2023-08-21 00:00:25">2023-08-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Opencv/">Opencv</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/Opencv/">Opencv</a></span></div><div class="content">
cv2.Scharr()
123456789101112131415161718192021222324#!/usr/bin/env python# -*- coding: UTF-8 -*-"""@Project ：Opencv学习 @File ：Scharr算子.py@IDE ：PyCharm @Author ：咋@Date ：2023/1/13 21:41 """import cv2import numpy as npimage = cv2.imread("Handsome.jpg")image = cv2.resize(image,(640,480))# scharr算子# 计算x方向dx = cv2.Scharr(image,-1,1,0)# 计算y方向dy = cv2.Scharr(image,-1,0,1)# 将两个方向加在一起，add方法sobel_img = cv2.add(dx,dy)# 展示图片cv2.imshow("sobel_img", ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2023/08/21/41-sobel%E7%AE%97%E5%AD%90/" title="24.sobel算子"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/27.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="24.sobel算子"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/08/21/41-sobel%E7%AE%97%E5%AD%90/" title="24.sobel算子">24.sobel算子</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-08-20T16:00:24.000Z" title="发表于 2023-08-21 00:00:24">2023-08-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-08-20T16:00:24.000Z" title="更新于 2023-08-21 00:00:24">2023-08-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Opencv/">Opencv</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/Opencv/">Opencv</a></span></div><div class="content">
边缘是像素值发生跃迁的位置，是图像的显著特征之一，在图像特征提取，对象检测，模式识别等方面都有重要的作用。sobel算子对图像求一阶导数。一阶导数越大，说明像素在该方向的变化越大，边缘信号越强。因为图像的灰度值都是离散的数字, sobel算子采用离散差分算子计算图像像素点亮度值的近似梯度.
soble x轴和y轴要分别计算,计算完用add添加在一起cv2.soble(image,desize,dx,dy)
123456789101112131415161718192021222324#!/usr/bin/env python# -*- coding: UTF-8 -*-"""@Project ：Opencv学习 @File ：sobel算子.py@IDE ：PyCharm @Author ：咋@Date ：2023/1/13 20:54 """import cv2import numpy as npimage = cv2.imread("Handsome.jpg")image = ...</div></div></div><div class="recent-post-item"><div class="post_cover left"><a href="/2023/08/21/40-%E5%8F%8C%E8%BE%B9%E6%BB%A4%E6%B3%A2/" title="23.双边滤波"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/30.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="23.双边滤波"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/08/21/40-%E5%8F%8C%E8%BE%B9%E6%BB%A4%E6%B3%A2/" title="23.双边滤波">23.双边滤波</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-08-20T16:00:23.000Z" title="发表于 2023-08-21 00:00:23">2023-08-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-08-20T16:00:23.000Z" title="更新于 2023-08-21 00:00:23">2023-08-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Opencv/">Opencv</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/Opencv/">Opencv</a></span></div><div class="content">
双边滤波可以保留边缘,同时可以对边缘内的区域进行平滑处理.cv2.bilateralFilter(src,d,sigmaColor,sigmaSpace[,dst[,boederType]])
123456789101112131415161718#!/usr/bin/env python# -*- coding: UTF-8 -*-"""@Project ：Opencv学习 @File ：双边滤波.py@IDE ：PyCharm @Author ：咋@Date ：2023/1/13 20:36 """import cv2import numpy as npimage = cv2.imread("Handsome.jpg")image = cv2.resize(image,(640,480))# 双边滤波bilateral = cv2.bilateralFilter(image,7,20,50)cv2.imshow("bilateral",np.hstack(( ...</div></div></div><div class="recent-post-item"><div class="post_cover right"><a href="/2023/08/21/39-%E4%B8%AD%E5%80%BC%E6%BB%A4%E6%B3%A2/" title="22.中值滤波"><img class="post-bg" src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/18.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="22.中值滤波"></a></div><div class="recent-post-info"><a class="article-title" href="/2023/08/21/39-%E4%B8%AD%E5%80%BC%E6%BB%A4%E6%B3%A2/" title="22.中值滤波">22.中值滤波</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time class="post-meta-date-created" datetime="2023-08-20T16:00:22.000Z" title="发表于 2023-08-21 00:00:22">2023-08-21</time><span class="article-meta-separator">|</span><i class="fas fa-history"></i><span class="article-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2023-08-20T16:00:22.000Z" title="更新于 2023-08-21 00:00:22">2023-08-21</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Opencv/">Opencv</a></span><span class="article-meta tags"><span class="article-meta-separator">|</span><i class="fas fa-tag"></i><a class="article-meta__tags" href="/tags/Opencv/">Opencv</a></span></div><div class="content">
中值滤波原理非常简单,，假设有一个数组[1556789],取其中的中间值(即中位数)作为卷积后的结果值即可.中值滤波对胡椒噪音(也叫椒盐噪音)效果明显.cv2.medianBlur(img,ksize)
注意ksize是一个数字
适合均匀的噪声，如椒盐噪声
12345678910111213141516171819#!/usr/bin/env python# -*- coding: UTF-8 -*-"""@Project ：Opencv学习 @File ：中值滤波.py@IDE ：PyCharm @Author ：咋@Date ：2023/1/13 20:33 """import cv2import numpy as npimage = cv2.imread("Handsome.jpg")image = cv2.resize(image,(640,480))# 中值滤波medianBulr = cv2.medianBlur(image,5)cv2.imshow("me ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">…</span><a class="page-number" href="/page/13/#content-inner">13</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src= "data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-lazy-src="https://raw.githubusercontent.com/lzypython/pic_bed/main/12.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">咋</div><div class="author-info__description">在心里种花，人生才不会枯萎</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">74</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">7</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">5</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/lzypython"><i class="fab fa-github"></i><span>🚗前往主页...</span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/lzypython" target="_blank" title="Github"><i class="fab fa-github" style="color: #24292e;"></i></a><a class="social-icon" href="mailto:a17730209318@163.com" target="_blank" title="Email"><i class="fas fa-envelope" style="color: #4a7dbe;"></i></a></div></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">希望你会喜欢这里</div></div><div class="sticky_layout"><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">74</div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2023-09-21T12:29:55.181Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer"><div id="footer-wrap"><div class="copyright">©2023 By 咋</div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="translateLink" type="button" title="简繁转换">繁</button><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside_config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js"></script><script src="/js/main.js"></script><script src="/js/tw_cn.js"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui/dist/fancybox/fancybox.umd.min.js"></script><script src="https://cdn.jsdelivr.net/npm/instant.page/instantpage.min.js" type="module"></script><script src="https://cdn.jsdelivr.net/npm/vanilla-lazyload/dist/lazyload.iife.min.js"></script><script src="https://cdn.jsdelivr.net/npm/node-snackbar/dist/snackbar.min.js"></script><script>function panguFn () {
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