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<!DOCTYPE html>
<html lang="cn">
<head>
<meta charset="utf-8" />
<title>python自动化测试人工智能</title>
<link rel="stylesheet" href="/theme/css/main.css" />
</head>
<body id="index" class="home">
<header id="banner" class="body">
<h1><a href="/">python自动化测试人工智能 </a></h1>
<nav><ul>
<li><a href="/category/ba-zi.html">八字</a></li>
<li><a href="/category/ce-shi.html">测试</a></li>
<li><a href="/category/ce-shi-kuang-jia.html">测试框架</a></li>
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<li><article class="hentry">
<header>
<h1><a href="/python_scrapy_cookbook2.html" rel="bookmark"
title="Permalink to python爬虫cookbook-爬虫入门">python爬虫cookbook-爬虫入门</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-08T11:20:00+08:00">
Published: 二 08 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <p>技术支持qq群:437355848 630011153 144081101</p>
<p><a href="https://china-testing.github.io/python_scrapy_cookbook2.html">本文最新版本</a></p>
<p>python爬虫cookbook第二章 数据采集和提取</p>
<p>在本章中,我们将介绍:</p>
<ul>
<li>如何使用BeautifulSoup解析网站并浏览DOM</li>
<li>用BeautifulSoup查找方法搜索DOM</li>
<li>用XPath和lxml查询DOM</li>
<li>使用XPath和CSS选择器查询数据</li>
<li>使用Scrapy选择器</li>
<li>以Unicode / UTF-8格式加载数据</li>
</ul>
<h3 id="beautifulsoupdom">用BeautifulSoup解析网站并浏览DOM</h3>
<p>01_bs_browser.py</p>
<table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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16</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="c1"># -*- coding: utf-8 -*-</span>
<span class="c1"># Author: china-testing#126.com wechat:pythontesting qq群:144081101</span>
<span class="c1"># CreateDate: 2018-05-08</span>
<span class="c1"># 02_blog_01_html.py</span>
<span class="kn">import …</span></pre></div></td></tr></table>
<a class="readmore" href="/python_scrapy_cookbook2.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python_scrapy_cookbook1.html" rel="bookmark"
title="Permalink to python爬虫cookbook-爬虫入门">python爬虫cookbook-爬虫入门</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-07T11:20:00+08:00">
Published: 一 07 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <p>技术支持qq群:437355848 630011153 144081101</p>
<p><a href="https://china-testing.github.io/python_scrapy_cookbook1.html">本文最新版本</a></p>
<p>第一章 爬虫入门</p>
<ul>
<li>Requests和Beautiful Soup 爬取python.org</li>
<li>urllib3和Beautiful Soup 爬取python.org</li>
<li>Scrapy 爬取python.org</li>
<li>Selenium和PhantomJs爬取Python.org</li>
</ul>
<p>请确认可以打开:https://www.python.org/events/pythonevents
安装好requests、bs4,然后我们开始实例1:Requests和Beautiful Soup 爬取python.org, 安装如果有问题尽量自己google,如果实在搞不定可以群里提问,或私聊咨询qq37391319, 咨询是需要收费,qq红包10元起</p>
<table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="c1"># pip3 install requests bs4</span>
</pre></div>
</td></tr></table>
<h3 id="requestsbeautiful-soup-pythonorg">Requests和Beautiful Soup 爬取python.org</h3>
<ul>
<li>目标: 爬取https …</li></ul>
<a class="readmore" href="/python_scrapy_cookbook1.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/testing_api_10min.html" rel="bookmark"
title="Permalink to 10分钟学会API测试">10分钟学会API测试</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-04T09:20:00+08:00">
Published: 五 04 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/ce-shi.html">测试</a>.</p>
</footer><!-- /.post-info --> <h2 id="api">什么是API</h2>
<p>在进行Api测试之前,让我们先了解一下什么是API?</p>
<p>API是应用程序编程接口(Application Programming Interface)的首字母缩写。</p>
<p>它支持两个独立软件系统之间的通信和数据交换。实现API的软件系统包含可以由其他软件系统执行的功能/子程序。</p>
<h2 id="api_1">什么是API测试?</h2>
<p>API测试与GUI测试完全不同,主要集中在软件架构的业务逻辑层。这种测试不太关注应用程序的外观和感觉。</p>
<p>在API测试中,您不必使用标准用户输入(键盘)和输出,而是使用软件将调用发送到API,获取输出并记下系统的响应。</p>
<p>在API测试中测试需要应用程序与API进行交互。为了测试API,你需要</p>
<ul>
<li>使用测试工具来驱动API</li>
<li>编写你自己的代码来测试API</li>
</ul>
<p><img alt="Alt Text" src="/images/testing_api_10min_level.png"> </p>
<h2 id="api_2">设置API测试环境</h2>
<ul>
<li>API测试与其他测试类型不同,因为GUI不可用,但您需要设置初始环境,以调用具有所需参数集的API,然后最终检查测试结果。</li>
<li>因此,为API测试设置测试环境似乎有点复杂。</li>
<li>数据库和服务器应按照应用程序要求进行配置。</li>
<li>安装完成后,应调用API函数来检查该API是否正常工作。</li>
</ul>
<h2 id="api_3">API的输出类型</h2>
<p>API的输出可能是</p>
<ul>
<li>任何类型的数据</li>
</ul>
<p>例如:有一个API函数应该为两个整数求和:</p>
<div class="highlight"><pre><span></span><span class="n">Long</span> <span class="n">add</span><span class="err">(</span><span class="kt">int</span> <span class="n">a</span><span class="err">,</span><span class="kt">int</span> <span class="n">b</span><span class="err">)</span>
</pre></div>
<p>数字必须作为输入参数给出。输出应该是两个整数的总和 …</p>
<a class="readmore" href="/testing_api_10min.html">read more</a>
</div><!-- /.entry-content -->
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<li><article class="hentry">
<header>
<h1><a href="/python_pandas_crash_tutorial4.html" rel="bookmark"
title="Permalink to python数据分析快速入门教程4-数据汇聚">python数据分析快速入门教程4-数据汇聚</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-03T11:20:00+08:00">
Published: 四 03 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <p><a href="https://china-testing.github.io/python_pandas_crash_tutorial.html">本书目录</a></p>
<p>我们需要的所有信息可能记录在单独的文件和数据帧中。例如,可能有一个公司信息单独表和股票价格表,数据被分成独立的表格以减少冗余信息。</p>
<h3 id="_1">连接</h3>
<ul>
<li>添加行</li>
</ul>
<p>4-1.py</p>
<div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/concat_1.csv'</span><span class="p">)</span>
<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/concat_2.csv'</span><span class="p">)</span>
<span class="n">df3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'data/concat_3.csv'</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">df2</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">df3</span><span class="p">)</span>
<span class="n">row_concat</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">df3</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">row_concat</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">row_concat</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="p">])</span>
<span class="n">new_row_series …</span></pre></div>
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<header>
<h1><a href="/python_pandas_crash_tutorial6.html" rel="bookmark"
title="Permalink to python数据分析快速入门教程6-重整">python数据分析快速入门教程6-重整</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-03T10:20:00+08:00">
Published: 四 03 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <h2 id="_1">介绍</h2>
<p>紧张整理中,多谢关注!</p>
<p>技术支持qq群:pandas数据分析scrapy爬虫 521070358 Python数据分析pandasopencv 6089740 python自动化测试Flask 144081101</p>
<p><a href="https://github.com/china-testing/python-api-tesing/tree/master/pandas/python_data_analyse_crash_course">源码下载</a></p>
<p><a href="https://china-testing.github.io/testing_training.html">接口自动化性能测试数据分析人工智能从业专家一对一线上培训大纲</a> </p>
<a class="readmore" href="/python_pandas_crash_tutorial6.html">read more</a>
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<li><article class="hentry">
<header>
<h1><a href="/python_opencv3_cookbook1.html" rel="bookmark"
title="Permalink to python opencv3 cookbook1: I/O和GUI">python opencv3 cookbook1: I/O和GUI</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-03T09:20:00+08:00">
Published: 四 03 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <ul>
<li>I/O和GUI 图像的基本操作以及视频:加载、保存和显示。</li>
<li>矩阵,颜色和过滤器,访问图像,通道和图像的区域、像素。各种颜色空间之间的转换和过滤器的使用</li>
<li>轮廓和分段。显示如何创建图像掩码,查找轮廓和细分图像。</li>
<li>对象检测和机器学习,描述检测和跟踪不同类型的物体,特别是(QR代码和ArUCo标记)</li>
<li>深度学习概述了OpenCV中的新功能与深层神经网络连接。加载深度学习模型并将其应用于计算机视觉任务。</li>
<li>线性代数。解决线性代数问题型并将其应用于计算机视觉。</li>
<li>测器和描述符 包含有关如何使用图像特征描述符:如何用它们进行计算、显示、匹配</li>
<li>图像和视频处理 图像序列并根据相关性得到结果序列。</li>
<li>多视图几何 如何使用摄像机检索关于场景的3D几何的信息</li>
</ul>
<p>第一章:I/O和GUI</p>
<h2 id="_1">介绍</h2>
<p>很少没有任何缺失值的数据集。 有许多缺失数据的表示。 在数据库中是NULL值,一些编程语言使用NA。缺失值可以是空字符串:''或者甚至是数值88或99等。Pandas显示缺失值为NaN。</p>
<p>本章将涵盖:</p>
<ul>
<li>从文件中读取图像</li>
<li>简单的图像转换 - 调整大小和翻转</li>
<li>使用有损和无损压缩保存图像</li>
<li>在OpenCV窗口中显示图像</li>
<li>使用UI元素 …</li></ul>
<a class="readmore" href="/python_opencv3_cookbook1.html">read more</a>
</div><!-- /.entry-content -->
</article></li>
<li><article class="hentry">
<header>
<h1><a href="/python_pandas_crash_tutorial5.html" rel="bookmark"
title="Permalink to python数据分析快速入门教程5-处理缺失数据">python数据分析快速入门教程5-处理缺失数据</a></h1>
</header>
<div class="entry-content">
<footer class="post-info">
<abbr class="published" title="2018-05-03T09:20:00+08:00">
Published: 四 03 五月 2018
</abbr>
<address class="vcard author">
By <a class="url fn" href="/author/andrew.html">andrew</a>
</address>
<p>In <a href="/category/shu-ju-fen-xi.html">数据分析</a>.</p>
</footer><!-- /.post-info --> <p><a href="https://china-testing.github.io/python_pandas_crash_tutorial.html">本书目录</a></p>
<p>第5章 缺失数据</p>
<h2 id="_1">介绍</h2>
<p>很少没有任何缺失值的数据集。 有许多缺失数据的表示。 在数据库中是NULL值,一些编程语言使用NA。缺失值可以是空字符串:''或者甚至是数值88或99等。Pandas显示缺失值为NaN。</p>
<p>本章将涵盖:</p>
<ul>
<li>什么是缺失值</li>
<li>如何创建缺失值</li>
<li>如何重新编码并使用缺失值进行计算</li>
</ul>
<h3 id="_2">什么是缺失值</h3>
<p>可以从numpy中获得NaN值,在Python中看到缺失值使用几种方式显示:NaN,NAN或nan,他们都是相等的。</p>
<p>NaN不等于0或空字符串''。</p>
<div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">NaN</span><span class="p">,</span> <span class="n">NAN</span><span class="p">,</span> <span class="n">nan</span>
<span class="n">In</span> <span class="p">[</span><span class="mi">2</span><span class="p">]:</span> <span class="k">print</span><span class="p">(</span><span class="n">NaN</span> <span class="o">==</span> <span class="bp">True</span><span class="p">,</span> <span class="n">NaN</span> <span class="o">==</span> <span class="bp">False</span><span class="p">,</span> <span class="n">NaN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">NaN</span> <span class="o">==</span> <span class="s1">''</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">'|'</span><span class="p">)</span>
<span class="bp">False</span><span class="o">|</span><span class="bp">False</span><span class="o">|</span><span class="bp">False</span><span class="o">|</span><span class="bp">False</span>
<span class="n">In</span> <span class="p">[</span><span class="mi">3</span><span class="p">]:</span> <span class="k">print</span><span class="p">(</span><span class="n">NaN</span> <span class="o">==</span> <span class="n">NaN …</span></pre></div>
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<h1><a href="/python3_lib_pytesseract.html" rel="bookmark"
title="Permalink to python库介绍-pytesseract: OCR光学字符识别">python库介绍-pytesseract: OCR光学字符识别</a></h1>
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<abbr class="published" title="2018-04-25T19:20:00+08:00">
Published: 三 25 四月 2018
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By <a class="url fn" href="/author/andrew.html">andrew</a>
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<p>In <a href="/category/python.html">python</a>.</p>
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<h2 id="_1">简介</h2>
<p>可以使用pytesseract库从图像中提取文本。<a href="https://github.com/tesseract-ocr/tesseract">Tesseract</a>是一款由Google赞助的开源OCR。 pytesseract是python包装器,它为可执行文件提供了pythonic API。</p>
<p>Tesseract(/'tesərækt/) 这个词的意思是"超立方体",指的是几何学里的四维标准方体,又称"正八胞体"。下图是一个正八胞体绕着两个四维空间中互相正交的平面进行双旋转时的透视投影。不过这里要讲的,是一款以其命名的开源 OCR(Optical Character Recognition, 光学字符识别) 软件。</p>
<p>所谓 OCR 是图像识别领域中的一个子领域,该领域专注于对图片中的文字信息进行识别并转换成能被常规文本编辑器编辑的文本。</p>
<p>Tesseract 已经有 30 年历史,开始它是惠普实验室的一款专利软件,然后在 2005 年开源,自 2006 年后由 Google 赞助进行后续的开发和维护。</p>
<p>在 1995 年 Tesseract 曾是世界前三的 OCR 引擎,而且在现在的免费 …</p>
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title="Permalink to 使用opencv转换3d图片">使用opencv转换3d图片</a></h1>
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<abbr class="published" title="2018-04-19T19:20:00+08:00">
Published: 四 19 四月 2018
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By <a class="url fn" href="/author/andrew.html">andrew</a>
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<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p>最近主流智能手机开始支持3D拍照,这些导出来16-bit .mhd/.raw的图片,无法直接查看,当然使用Fiji/ImageJ可以粗略的查看,但是操作不太方便。</p>
<p>这里我们以vivo x21为例进行图片转换。</p>
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<h2 id="rawdepthir">切分raw为depth和ir</h2>
<p>split_raw.py </p>
<table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
2
3
4
5
6
7
8
9
10
11
12
13
14</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">from</span> <span class="nn">photos …</span></pre></div></td></tr></table>
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<h1><a href="/python_opencv3_exmaple2.html" rel="bookmark"
title="Permalink to python opencv3实例(对象识别和增强现实)2-边缘检测和应用图像过滤器">python opencv3实例(对象识别和增强现实)2-边缘检测和应用图像过滤器</a></h1>
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<abbr class="published" title="2018-04-17T19:20:00+08:00">
Published: 二 17 四月 2018
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<p>In <a href="/category/python.html">python</a>.</p>
</footer><!-- /.post-info --> <p><a href="https://github.com/PacktPublishing/OpenCV-3-x-with-Python-By-Example">原书代码地址</a></p>
<p>在本章中,我们将看到如何将酷视觉效果应用于图片。 我们将学习如何使用基本的图像处理操作器,讨论边缘检测,并了解我们如何使用图像滤镜来应用各种图像影响照片。</p>
<p>在本章的最后,你会知道:
* 2D卷积是什么,以及如何使用它
* 如何模糊图像
* 如何检测图像中的边缘
* 如何将运动模糊应用于图像
* 如何锐化和浮雕图像
* 如何侵蚀和扩大形象
* 如何创建晕影过滤器
* 如何增强图像对比度</p>
<h2 id="2d">2D卷积</h2>
<p>卷积是图像处理中的基本操作,是我们对每个像素应用数学运算符,并在一些像素中更改其值办法。</p>
<p><img alt="Alt Text" src="/images/python_opencv_example2_kernel.PNG"> </p>
<p><img alt="Alt Text" src="/images/python_opencv_example2_filter.PNG"> </p>
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