百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别
Day4-《青春有你2》作业：五人识别
- 要求
- 本地操作
- 爬取五位选手的图片
- PIL处理图片实现数增强
- 将图片地址和标记写入文件
- AIStudio平台操作
- 上传数据集
- AIStudio代码
- 识别结果
- 隔天更新
- 策略配置
大家好，我是EverdayForCode。你，今天学习了吗？
要求
- 自己准备数据集
- 完成《青春有你2》前五名照片识别
- 虞书欣
- 许佳琪
- 赵小棠
- 安崎
- 王承渲
本地操作
爬取五位选手的图片
import json
import re
import requests
import datetime
from bs4 import BeautifulSoup
import os
# 获取当天的日期,并进行格式化,用于后面文件命名，格式:20200420
today = datetime.date.today().strftime('%Y%m%d')
# %%
def crawl_wiki_data(url):
"""
爬取必应图片
"""
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:75.0) Gecko/20100101 Firefox/75.0'
}
url = url
try:
response = requests.get(url, headers=headers)
print(response.status_code)
html = response.content
# 将一段文档传入BeautifulSoup的构造方法,就能得到一个文档的对象, 可以传入一段字符串
soup = BeautifulSoup(html, 'lxml')
title = soup.find_all('title')
title = str(title).replace(''</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span>
title <span class="token operator">=</span> title<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'['</span><span class="token punctuation">,</span> <span class="token string">''</span><span class="token punctuation">)</span>
str_temp <span class="token operator">=</span> title<span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">'-'</span><span class="token punctuation">)</span>
name <span class="token operator">=</span> str_temp<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token operator">-</span><span class="token number">1</span><span class="token punctuation">]</span>
imgs <span class="token operator">=</span> soup<span class="token punctuation">.</span>find_all<span class="token punctuation">(</span><span class="token string">'img'</span><span class="token punctuation">,</span> <span class="token punctuation">{</span><span class="token string">'class'</span><span class="token punctuation">:</span> <span class="token string">'mimg vimgld'</span><span class="token punctuation">}</span><span class="token punctuation">)</span>
<span class="token comment"># print(imgs)</span>
srcs <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
count <span class="token operator">=</span> <span class="token number">0</span>
<span class="token keyword">for</span> img <span class="token keyword">in</span> imgs<span class="token punctuation">:</span>
src <span class="token operator">=</span> img<span class="token punctuation">.</span>get<span class="token punctuation">(</span><span class="token string">'data-src'</span><span class="token punctuation">)</span>
<span class="token keyword">if</span> src <span class="token keyword">is</span> <span class="token operator">not</span> <span class="token boolean">None</span><span class="token punctuation">:</span>
star <span class="token operator">=</span> <span class="token punctuation">{</span><span class="token string">'link'</span><span class="token punctuation">:</span> src<span class="token punctuation">,</span> <span class="token string">'name'</span><span class="token punctuation">:</span> name<span class="token punctuation">}</span>
<span class="token comment"># print(src)</span>
srcs<span class="token punctuation">.</span>append<span class="token punctuation">(</span>star<span class="token punctuation">)</span>
count <span class="token operator">+=</span> <span class="token number">1</span>
<span class="token keyword">if</span> count <span class="token operator">></span> <span class="token number">55</span><span class="token punctuation">:</span>
<span class="token keyword">break</span>
<span class="token keyword">return</span> srcs
<span class="token keyword">except</span> Exception <span class="token keyword">as</span> e<span class="token punctuation">:</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>e<span class="token punctuation">)</span>
<span class="token comment"># %%</span>
<span class="token comment"># srcs = crawl_wiki_data('https://cn.bing.com/images/search?q=%E8%99%9E%E4%B9%A6%E6%AC%A3&form=HDRSC2&first=1&cw=1519&ch=258')</span>
<span class="token comment"># print(srcs)</span>
<span class="token comment"># %%</span>
<span class="token keyword">def</span> <span class="token function">parse_wiki_data</span><span class="token punctuation">(</span>srcs<span class="token operator">=</span><span class="token punctuation">[</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token triple-quoted-string string">'''
，存JSON文件,保存到work目录下
'''</span>
json_data <span class="token operator">=</span> json<span class="token punctuation">.</span>loads<span class="token punctuation">(</span><span class="token builtin">str</span><span class="token punctuation">(</span>srcs<span class="token punctuation">)</span><span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">"\'"</span><span class="token punctuation">,</span> <span class="token string">"\""</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token keyword">with</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'work/'</span> <span class="token operator">+</span> today <span class="token operator">+</span> <span class="token string">'.json'</span><span class="token punctuation">,</span> <span class="token string">'w'</span><span class="token punctuation">,</span> encoding<span class="token operator">=</span><span class="token string">'UTF-8'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> f<span class="token punctuation">:</span>
json<span class="token punctuation">.</span>dump<span class="token punctuation">(</span>json_data<span class="token punctuation">,</span> f<span class="token punctuation">,</span> ensure_ascii<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">)</span>
<span class="token comment"># %%</span>
<span class="token comment"># 测试</span>
<span class="token comment"># srcs = crawl_wiki_data(</span>
<span class="token comment"># 'https://cn.bing.com/images/search?q=%E8%99%9E%E4%B9%A6%E6%AC%A3&form=HDRSC2&first=1&cw=1519&ch=258')</span>
<span class="token comment"># parse_wiki_data(srcs)</span>
<span class="token comment"># %%</span>
<span class="token keyword">def</span> <span class="token function">crawl_pic_urls</span><span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token triple-quoted-string string">'''
爬取每个选手的图片，并保存
'''</span>
<span class="token keyword">with</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'work/'</span> <span class="token operator">+</span> today <span class="token operator">+</span> <span class="token string">'.json'</span><span class="token punctuation">,</span> <span class="token string">'r'</span><span class="token punctuation">,</span> encoding<span class="token operator">=</span><span class="token string">'UTF-8'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> <span class="token builtin">file</span><span class="token punctuation">:</span>
json_array <span class="token operator">=</span> json<span class="token punctuation">.</span>loads<span class="token punctuation">(</span><span class="token builtin">file</span><span class="token punctuation">.</span>read<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
headers <span class="token operator">=</span> <span class="token punctuation">{</span>
<span class="token string">'User-Agent'</span><span class="token punctuation">:</span> <span class="token string">'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:75.0) Gecko/20100101 Firefox/75.0'</span>
<span class="token punctuation">}</span>
count <span class="token operator">=</span> <span class="token number">0</span>
<span class="token keyword">for</span> star <span class="token keyword">in</span> json_array<span class="token punctuation">:</span>
name <span class="token operator">=</span> star<span class="token punctuation">[</span><span class="token string">'name'</span><span class="token punctuation">]</span>
link <span class="token operator">=</span> star<span class="token punctuation">[</span><span class="token string">'link'</span><span class="token punctuation">]</span>
<span class="token comment"># ！！！根据图片链接列表pic_urls, 下载所有图片，保存在以name命名的文件夹中！！！</span>
down_pic<span class="token punctuation">(</span>name<span class="token punctuation">,</span> link<span class="token punctuation">,</span> count<span class="token punctuation">)</span>
count <span class="token operator">+=</span> <span class="token number">1</span>
<span class="token comment"># %%</span>
<span class="token comment"># 下载图片</span>
<span class="token keyword">def</span> <span class="token function">down_pic</span><span class="token punctuation">(</span>name<span class="token punctuation">,</span> link<span class="token punctuation">,</span> i<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token triple-quoted-string string">'''
根据图片链接列表pic_urls, 下载所有图片，保存在以name命名的文件夹中,
'''</span>
path <span class="token operator">=</span> <span class="token string">'work/'</span> <span class="token operator">+</span> <span class="token string">'pics/'</span> <span class="token operator">+</span> name <span class="token operator">+</span> <span class="token string">'/'</span>
<span class="token keyword">if</span> <span class="token operator">not</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>exists<span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
os<span class="token punctuation">.</span>makedirs<span class="token punctuation">(</span>path<span class="token punctuation">)</span>
<span class="token keyword">try</span><span class="token punctuation">:</span>
pic <span class="token operator">=</span> requests<span class="token punctuation">.</span>get<span class="token punctuation">(</span>link<span class="token punctuation">,</span> timeout<span class="token operator">=</span><span class="token number">15</span><span class="token punctuation">)</span>
string <span class="token operator">=</span> <span class="token builtin">str</span><span class="token punctuation">(</span>i <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span> <span class="token operator">+</span> <span class="token string">'.jpg'</span>
<span class="token keyword">with</span> <span class="token builtin">open</span><span class="token punctuation">(</span>path <span class="token operator">+</span> string<span class="token punctuation">,</span> <span class="token string">'wb'</span><span class="token punctuation">)</span> <span class="token keyword">as</span> f<span class="token punctuation">:</span>
f<span class="token punctuation">.</span>write<span class="token punctuation">(</span>pic<span class="token punctuation">.</span>content<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'成功下载第%s张图片: %s'</span> <span class="token operator">%</span> <span class="token punctuation">(</span><span class="token builtin">str</span><span class="token punctuation">(</span>i <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token builtin">str</span><span class="token punctuation">(</span>link<span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token keyword">except</span> Exception <span class="token keyword">as</span> e<span class="token punctuation">:</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">'下载第%s张图片时失败: %s'</span> <span class="token operator">%</span> <span class="token punctuation">(</span><span class="token builtin">str</span><span class="token punctuation">(</span>i <span class="token operator">+</span> <span class="token number">1</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token builtin">str</span><span class="token punctuation">(</span>link<span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>e<span class="token punctuation">)</span>
<span class="token comment"># %%</span>
<span class="token keyword">def</span> <span class="token function">show_pic_path</span><span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token triple-quoted-string string">'''
遍历所爬取的每张图片，并打印所有图片的绝对路径
'''</span>
pic_num <span class="token operator">=</span> <span class="token number">0</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> dirnames<span class="token punctuation">,</span> filenames<span class="token punctuation">)</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>walk<span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token keyword">for</span> filename <span class="token keyword">in</span> filenames<span class="token punctuation">:</span>
pic_num <span class="token operator">+=</span> <span class="token number">1</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"第%d张照片：%s"</span> <span class="token operator">%</span> <span class="token punctuation">(</span>pic_num<span class="token punctuation">,</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>join<span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> filename<span class="token punctuation">)</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"共爬取《青春有你2》前五名选手的%d照片"</span> <span class="token operator">%</span> pic_num<span class="token punctuation">)</span>
<span class="token comment"># %%</span>
<span class="token comment"># 测试</span>
url_list <span class="token operator">=</span> <span class="token punctuation">[</span>
<span class="token string">'https://cn.bing.com/images/search?q=%E8%99%9E%E4%B9%A6%E6%AC%A3&form=HDRSC2&first=1&cw=1519&ch=258'</span><span class="token punctuation">,</span>
<span class="token string">'https://cn.bing.com/images/search?q=%E8%AE%B8%E4%BD%B3%E7%90%AA&go=%E6%90%9C%E7%B4%A2&qs=ds&form=QBIR&first=1'</span><span class="token punctuation">,</span>
<span class="token string">'https://cn.bing.com/images/search?q=%E7%8E%8B%E6%89%BF%E6%B8%B2&qs=n&form=QBIRMH&sp=-1&pq=%E7%8E%8B%E6%89%BF%E6%B8%B2&sc=0-3&sk=&cvid=32CFFC40D3E843FF831F1EBEDE9C0DA1'</span><span class="token punctuation">,</span>
<span class="token string">'https://cn.bing.com/images/search?q=%E8%B5%B5%E5%B0%8F%E6%A3%A0&qs=n&form=QBIR&sp=-1&pq=%E8%B5%B5%E5%B0%8F%E6%A3%A0&sc=2-3&sk=&cvid=8817542052FD499595F1B95F279526EF'</span>
<span class="token punctuation">]</span>
srcs <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
<span class="token keyword">for</span> url <span class="token keyword">in</span> url_list<span class="token punctuation">:</span>
src_list <span class="token operator">=</span> crawl_wiki_data<span class="token punctuation">(</span>url<span class="token punctuation">)</span>
srcs<span class="token punctuation">.</span>extend<span class="token punctuation">(</span>src_list<span class="token punctuation">)</span>
parse_wiki_data<span class="token punctuation">(</span>srcs<span class="token punctuation">)</span>
crawl_pic_urls<span class="token punctuation">(</span><span class="token punctuation">)</span>
show_pic_path<span class="token punctuation">(</span><span class="token string">'work'</span><span class="token punctuation">)</span>
</code></pre>
<p>由于必应图片几乎是动态加载，EverdayForCode爬虫很菜，每一个选手只获取了20几张图片（安琦一张没有爬取，应为安琦的照片大多是和其他人的合照，后续手动下载）<br> 每个选手20几张图片做训练是远远不够的，因此需要使用数据增强</p>
<h4>PIL处理图片实现数增强</h4>
<pre><code class="prism language-python"><span class="token keyword">import</span> os
<span class="token keyword">from</span> PIL <span class="token keyword">import</span> Image
<span class="token keyword">from</span> PIL <span class="token keyword">import</span> ImageEnhance
<span class="token comment">#%%</span>
<span class="token comment"># 上下左右旋转图片</span>
<span class="token keyword">def</span> <span class="token function">Rotate_pic_up_down_bri</span><span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> dirnames<span class="token punctuation">,</span> filenames<span class="token punctuation">)</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>walk<span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token keyword">for</span> filename <span class="token keyword">in</span> filenames<span class="token punctuation">:</span>
img_path <span class="token operator">=</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>join<span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> filename<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>img_path<span class="token punctuation">)</span>
<span class="token comment"># 打开图片</span>
img <span class="token operator">=</span> Image<span class="token punctuation">.</span><span class="token builtin">open</span><span class="token punctuation">(</span>img_path<span class="token punctuation">)</span>
img <span class="token operator">=</span> img<span class="token punctuation">.</span>convert<span class="token punctuation">(</span><span class="token string">'RGB'</span><span class="token punctuation">)</span>
<span class="token comment"># 左右镜像</span>
img_lr <span class="token operator">=</span> img<span class="token punctuation">.</span>transpose<span class="token punctuation">(</span>Image<span class="token punctuation">.</span>FLIP_LEFT_RIGHT<span class="token punctuation">)</span>
<span class="token comment">#写入图片</span>
w_path <span class="token operator">=</span> <span class="token string">'img_e/'</span><span class="token operator">+</span>path<span class="token operator">+</span><span class="token string">'/'</span>
<span class="token keyword">if</span> <span class="token operator">not</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>exists<span class="token punctuation">(</span>w_path<span class="token punctuation">)</span><span class="token punctuation">:</span>
os<span class="token punctuation">.</span>makedirs<span class="token punctuation">(</span>w_path<span class="token punctuation">)</span>
string <span class="token operator">=</span> filename<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'.jpg'</span><span class="token punctuation">,</span><span class="token string">'_lr.jpg'</span><span class="token punctuation">)</span>
img_lr<span class="token punctuation">.</span>save<span class="token punctuation">(</span>w_path<span class="token operator">+</span>string<span class="token punctuation">)</span>
<span class="token comment"># 上下镜像</span>
img_bt <span class="token operator">=</span> img<span class="token punctuation">.</span>transpose<span class="token punctuation">(</span>Image<span class="token punctuation">.</span>FLIP_TOP_BOTTOM<span class="token punctuation">)</span>
string <span class="token operator">=</span> filename<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'.jpg'</span><span class="token punctuation">,</span> <span class="token string">'_bt.jpg'</span><span class="token punctuation">)</span>
img_bt<span class="token punctuation">.</span>save<span class="token punctuation">(</span>w_path <span class="token operator">+</span> string<span class="token punctuation">)</span>
<span class="token comment"># 增强亮度</span>
<span class="token comment"># 增强亮度</span>
enh_bri <span class="token operator">=</span> ImageEnhance<span class="token punctuation">.</span>Brightness<span class="token punctuation">(</span>img<span class="token punctuation">)</span>
brightness <span class="token operator">=</span> <span class="token number">1.5</span>
img_bri <span class="token operator">=</span> enh_bri<span class="token punctuation">.</span>enhance<span class="token punctuation">(</span>brightness<span class="token punctuation">)</span>
string <span class="token operator">=</span> filename<span class="token punctuation">.</span>replace<span class="token punctuation">(</span><span class="token string">'.jpg'</span><span class="token punctuation">,</span> <span class="token string">'_bri.jpg'</span><span class="token punctuation">)</span>
img_bri<span class="token punctuation">.</span>save<span class="token punctuation">(</span>w_path <span class="token operator">+</span> string<span class="token punctuation">)</span>
<span class="token comment">#%%</span>
Rotate_pic_up_down_bri<span class="token punctuation">(</span><span class="token string">'img/anqi'</span><span class="token punctuation">)</span>
Rotate_pic_up_down_bri<span class="token punctuation">(</span><span class="token string">'img/wangchengxuan'</span><span class="token punctuation">)</span>
Rotate_pic_up_down_bri<span class="token punctuation">(</span><span class="token string">'img/xujiaqi'</span><span class="token punctuation">)</span>
Rotate_pic_up_down_bri<span class="token punctuation">(</span><span class="token string">'img/yushuxin'</span><span class="token punctuation">)</span>
Rotate_pic_up_down_bri<span class="token punctuation">(</span><span class="token string">'img/zhaoxiaotang'</span><span class="token punctuation">)</span>
</code></pre>
<p>使用PIL实现了图片的上下旋转、左右旋转、亮度增强</p>
<h4>将图片地址和标记写入文件</h4>
<p>由于是使用PaddleHub里面的模型，因此自定义数据需要遵守相应的数据规范，详见：</p>
<blockquote>
<p>数据规范<br> <a href="http://img.e-com-net.com/image/info8/21f4c7f49108488db874d95b94fc77b9.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/21f4c7f49108488db874d95b94fc77b9.jpg" alt="百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别_第1张图片" width="650" height="383" style="border:1px solid black;"></a></p>
</blockquote>
<h3>AIStudio平台操作</h3>
<h4>上传数据集</h4>
<p>在本地做好数据及集，上传至AIStudion</p>
<h4>AIStudio代码</h4>
<pre><code class="prism language-python"><span class="token comment">#CPU环境启动请务必执行该指令</span>
<span class="token operator">%</span>set_env CPU_NUM<span class="token operator">=</span><span class="token number">1</span>
<span class="token comment">#安装paddlehub</span>
!pip install paddlehub<span class="token operator">==</span><span class="token number">1.6</span><span class="token number">.0</span> <span class="token operator">-</span>i https<span class="token punctuation">:</span><span class="token operator">//</span>pypi<span class="token punctuation">.</span>tuna<span class="token punctuation">.</span>tsinghua<span class="token punctuation">.</span>edu<span class="token punctuation">.</span>cn<span class="token operator">/</span>simple
<span class="token comment"># 解压数据集</span>
!unzip <span class="token operator">-</span>o <span class="token operator">/</span>home<span class="token operator">/</span>aistudio<span class="token operator">/</span>data<span class="token operator">/</span>img<span class="token punctuation">.</span><span class="token builtin">zip</span> <span class="token operator">-</span>d <span class="token punctuation">.</span><span class="token operator">/</span>dataset<span class="token operator">/</span>img
<span class="token comment"># 遍历图集写入自定义数据</span>
<span class="token keyword">import</span> os
<span class="token keyword">def</span> <span class="token function">writeText</span><span class="token punctuation">(</span>path<span class="token punctuation">,</span>label<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token keyword">for</span> <span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> dirnames<span class="token punctuation">,</span> filenames<span class="token punctuation">)</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>walk<span class="token punctuation">(</span>path<span class="token punctuation">)</span><span class="token punctuation">:</span>
i <span class="token operator">=</span> <span class="token number">1</span>
<span class="token keyword">for</span> filename <span class="token keyword">in</span> filenames<span class="token punctuation">:</span>
img_path <span class="token operator">=</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>join<span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> filename<span class="token punctuation">)</span>
<span class="token comment"># print(img_path)</span>
tra_f <span class="token operator">=</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/train_list.txt'</span><span class="token punctuation">,</span><span class="token string">'a'</span><span class="token punctuation">)</span>
tes_f <span class="token operator">=</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/test_list.txt'</span><span class="token punctuation">,</span><span class="token string">'a'</span><span class="token punctuation">)</span>
val_f <span class="token operator">=</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/validate_list.txt'</span><span class="token punctuation">,</span><span class="token string">'a'</span><span class="token punctuation">)</span>
string <span class="token operator">=</span> img_path <span class="token operator">+</span> <span class="token string">' '</span> <span class="token operator">+</span> <span class="token builtin">str</span><span class="token punctuation">(</span>label<span class="token punctuation">)</span> <span class="token operator">+</span> <span class="token string">'\n'</span>
<span class="token keyword">if</span> <span class="token number">1</span> <span class="token operator"><=</span> i <span class="token operator">%</span> <span class="token number">10</span> <span class="token operator"><=</span> <span class="token number">8</span><span class="token punctuation">:</span>
tra_f<span class="token punctuation">.</span>write<span class="token punctuation">(</span>string<span class="token punctuation">)</span>
<span class="token keyword">elif</span> i <span class="token operator">%</span> <span class="token number">10</span> <span class="token operator">==</span> <span class="token number">9</span><span class="token punctuation">:</span>
tes_f<span class="token punctuation">.</span>write<span class="token punctuation">(</span>string<span class="token punctuation">)</span>
<span class="token keyword">else</span><span class="token punctuation">:</span>
val_f<span class="token punctuation">.</span>write<span class="token punctuation">(</span>string<span class="token punctuation">)</span>
i <span class="token operator">+=</span> <span class="token number">1</span>
tra_f<span class="token punctuation">.</span>close<span class="token punctuation">(</span><span class="token punctuation">)</span>
tes_f<span class="token punctuation">.</span>close<span class="token punctuation">(</span><span class="token punctuation">)</span>
val_f<span class="token punctuation">.</span>close<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token comment"># 写入数据并查看 </span>
writeText<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/img/wangchengxuan'</span><span class="token punctuation">,</span><span class="token number">4</span><span class="token punctuation">)</span>
writeText<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/img/zhaoxiaotang'</span><span class="token punctuation">,</span><span class="token number">2</span><span class="token punctuation">)</span>
writeText<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/img/xujiaqi'</span><span class="token punctuation">,</span><span class="token number">1</span><span class="token punctuation">)</span>
writeText<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/img/yushuxin'</span><span class="token punctuation">,</span><span class="token number">0</span><span class="token punctuation">)</span>
writeText<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/img/anqi'</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">)</span>
!cat <span class="token operator">/</span>home<span class="token operator">/</span>aistudio<span class="token operator">/</span>dataset<span class="token operator">/</span>train_list<span class="token punctuation">.</span>txt
!cat <span class="token operator">/</span>home<span class="token operator">/</span>aistudio<span class="token operator">/</span>dataset<span class="token operator">/</span>test_list<span class="token punctuation">.</span>txt
!cat <span class="token operator">/</span>home<span class="token operator">/</span>aistudio<span class="token operator">/</span>dataset<span class="token operator">/</span>validate_list<span class="token punctuation">.</span>txt
<span class="token triple-quoted-string string">'''
Step2、加载预训练模型
接下来我们要在PaddleHub中选择合适的预训练模型来Finetune，由于是图像分类任务，因此我们使用经典的ResNet-50作为预训练模型。PaddleHub提供了丰富的图像分类预训练模型，包括了最新的神经网络架构搜索类的PNASNet，我们推荐您尝试不同的预训练模型来获得更好的性能。
'''</span>
<span class="token keyword">import</span> paddlehub <span class="token keyword">as</span> hub
module <span class="token operator">=</span> hub<span class="token punctuation">.</span>Module<span class="token punctuation">(</span>name<span class="token operator">=</span><span class="token string">"resnet_v2_50_imagenet"</span><span class="token punctuation">)</span>
<span class="token triple-quoted-string string">'''
Step3、数据准备
接着需要加载图片数据集。我们使用自定义的数据进行体验，请查看适配自定义数据
'''</span>
<span class="token keyword">from</span> paddlehub<span class="token punctuation">.</span>dataset<span class="token punctuation">.</span>base_cv_dataset <span class="token keyword">import</span> BaseCVDataset
<span class="token keyword">class</span> <span class="token class-name">DemoDataset</span><span class="token punctuation">(</span>BaseCVDataset<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token keyword">def</span> <span class="token function">__init__</span><span class="token punctuation">(</span>self<span class="token punctuation">)</span><span class="token punctuation">:</span>
<span class="token comment"># 数据集存放位置</span>
self<span class="token punctuation">.</span>dataset_dir <span class="token operator">=</span> <span class="token string">"dataset"</span>
<span class="token builtin">super</span><span class="token punctuation">(</span>DemoDataset<span class="token punctuation">,</span> self<span class="token punctuation">)</span><span class="token punctuation">.</span>__init__<span class="token punctuation">(</span>
base_path<span class="token operator">=</span>self<span class="token punctuation">.</span>dataset_dir<span class="token punctuation">,</span>
train_list_file<span class="token operator">=</span><span class="token string">"train_list.txt"</span><span class="token punctuation">,</span>
validate_list_file<span class="token operator">=</span><span class="token string">"validate_list.txt"</span><span class="token punctuation">,</span>
test_list_file<span class="token operator">=</span><span class="token string">"test_list.txt"</span><span class="token punctuation">,</span>
label_list_file<span class="token operator">=</span><span class="token string">"label_list.txt"</span><span class="token punctuation">,</span>
<span class="token punctuation">)</span>
dataset <span class="token operator">=</span> DemoDataset<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token triple-quoted-string string">'''
Step4、生成数据读取器
接着生成一个图像分类的reader，reader负责将dataset的数据进行预处理，接着以特定格式组织并输入给模型进行训练。
当我们生成一个图像分类的reader时，需要指定输入图片的大小
'''</span>
data_reader <span class="token operator">=</span> hub<span class="token punctuation">.</span>reader<span class="token punctuation">.</span>ImageClassificationReader<span class="token punctuation">(</span>
image_width<span class="token operator">=</span>module<span class="token punctuation">.</span>get_expected_image_width<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
image_height<span class="token operator">=</span>module<span class="token punctuation">.</span>get_expected_image_height<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
images_mean<span class="token operator">=</span>module<span class="token punctuation">.</span>get_pretrained_images_mean<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
images_std<span class="token operator">=</span>module<span class="token punctuation">.</span>get_pretrained_images_std<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
dataset<span class="token operator">=</span>dataset<span class="token punctuation">)</span>
<span class="token triple-quoted-string string">'''
Step5、配置策略
在进行Finetune前，我们可以设置一些运行时的配置，例如如下代码中的配置，表示：
use_cuda：设置为False表示使用CPU进行训练。如果您本机支持GPU，且安装的是GPU版本的PaddlePaddle，我们建议您将这个选项设置为True；
epoch：迭代轮数；
batch_size：每次训练的时候，给模型输入的每批数据大小为32，模型训练时能够并行处理批数据，因此batch_size越大，训练的效率越高，但是同时带来了内存的负荷，过大的batch_size可能导致内存不足而无法训练，因此选择一个合适的batch_size是很重要的一步；
log_interval：每隔10 step打印一次训练日志；
eval_interval：每隔50 step在验证集上进行一次性能评估；
checkpoint_dir：将训练的参数和数据保存到cv_finetune_turtorial_demo目录中；
strategy：使用DefaultFinetuneStrategy策略进行finetune；
更多运行配置，请查看RunConfig
同时PaddleHub提供了许多优化策略，如AdamWeightDecayStrategy、ULMFiTStrategy、DefaultFinetuneStrategy等，详细信息参见策略
'''</span>
config <span class="token operator">=</span> hub<span class="token punctuation">.</span>RunConfig<span class="token punctuation">(</span>
use_cuda<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">,</span> <span class="token comment">#是否使用GPU训练，默认为False；</span>
num_epoch<span class="token operator">=</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token comment">#Fine-tune的轮数；</span>
checkpoint_dir<span class="token operator">=</span><span class="token string">"cv_finetune_turtorial_demo"</span><span class="token punctuation">,</span><span class="token comment">#模型checkpoint保存路径, 若用户没有指定，程序会自动生成；</span>
batch_size<span class="token operator">=</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token comment">#训练的批大小，如果使用GPU，请根据实际情况调整batch_size；</span>
eval_interval<span class="token operator">=</span><span class="token number">10</span><span class="token punctuation">,</span> <span class="token comment">#模型评估的间隔，默认每100个step评估一次验证集；</span>
strategy<span class="token operator">=</span>hub<span class="token punctuation">.</span>finetune<span class="token punctuation">.</span>strategy<span class="token punctuation">.</span>DefaultFinetuneStrategy<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment">#Fine-tune优化策略；</span>
<span class="token triple-quoted-string string">'''
Step6、组建Finetune Task
有了合适的预训练模型和准备要迁移的数据集后，我们开始组建一个Task。
由于该数据设置是一个二分类的任务，而我们下载的分类module是在ImageNet数据集上训练的千分类模型，所以我们需要对模型进行简单的微调，把模型改造为一个二分类模型：
获取module的上下文环境，包括输入和输出的变量，以及Paddle Program；
从输出变量中找到特征图提取层feature_map；
在feature_map后面接入一个全连接层，生成Task；
'''</span>
input_dict<span class="token punctuation">,</span> output_dict<span class="token punctuation">,</span> program <span class="token operator">=</span> module<span class="token punctuation">.</span>context<span class="token punctuation">(</span>trainable<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
img <span class="token operator">=</span> input_dict<span class="token punctuation">[</span><span class="token string">"image"</span><span class="token punctuation">]</span>
feature_map <span class="token operator">=</span> output_dict<span class="token punctuation">[</span><span class="token string">"feature_map"</span><span class="token punctuation">]</span>
feed_list <span class="token operator">=</span> <span class="token punctuation">[</span>img<span class="token punctuation">.</span>name<span class="token punctuation">]</span>
task <span class="token operator">=</span> hub<span class="token punctuation">.</span>ImageClassifierTask<span class="token punctuation">(</span>
data_reader<span class="token operator">=</span>data_reader<span class="token punctuation">,</span>
feed_list<span class="token operator">=</span>feed_list<span class="token punctuation">,</span>
feature<span class="token operator">=</span>feature_map<span class="token punctuation">,</span>
num_classes<span class="token operator">=</span>dataset<span class="token punctuation">.</span>num_labels<span class="token punctuation">,</span>
config<span class="token operator">=</span>config<span class="token punctuation">)</span>
<span class="token triple-quoted-string string">'''
Step5、开始Finetune
我们选择finetune_and_eval接口来进行模型训练，这个接口在finetune的过程中，会周期性的进行模型效果的评估，以便我们了解整个训练过程的性能变化。
'''</span>
run_states <span class="token operator">=</span> task<span class="token punctuation">.</span>finetune_and_eval<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token triple-quoted-string string">'''
Step6、预测
当Finetune完成后，我们使用模型来进行预测，先通过以下命令来获取测试的图片
'''</span>
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np
<span class="token keyword">import</span> matplotlib<span class="token punctuation">.</span>pyplot <span class="token keyword">as</span> plt
<span class="token keyword">import</span> matplotlib<span class="token punctuation">.</span>image <span class="token keyword">as</span> mpimg
<span class="token comment"># 生成测试文件</span>
<span class="token keyword">import</span> os
<span class="token keyword">for</span> <span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> dirnames<span class="token punctuation">,</span> filenames<span class="token punctuation">)</span> <span class="token keyword">in</span> os<span class="token punctuation">.</span>walk<span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/test'</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
i <span class="token operator">=</span> <span class="token number">0</span>
pics_f <span class="token operator">=</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/test/test_pics.txt'</span><span class="token punctuation">,</span><span class="token string">'w'</span><span class="token punctuation">)</span>
<span class="token keyword">for</span> filename <span class="token keyword">in</span> filenames<span class="token punctuation">:</span>
img_path <span class="token operator">=</span> os<span class="token punctuation">.</span>path<span class="token punctuation">.</span>join<span class="token punctuation">(</span>dirpath<span class="token punctuation">,</span> filename<span class="token punctuation">)</span>
string <span class="token operator">=</span> img_path <span class="token operator">+</span> <span class="token string">' '</span> <span class="token operator">+</span> <span class="token builtin">str</span><span class="token punctuation">(</span>i<span class="token punctuation">)</span> <span class="token operator">+</span> <span class="token string">'\n'</span>
pics_f<span class="token punctuation">.</span>write<span class="token punctuation">(</span>string<span class="token punctuation">)</span>
i <span class="token operator">+=</span> <span class="token number">1</span>
pics_f<span class="token punctuation">.</span>close<span class="token punctuation">(</span><span class="token punctuation">)</span>
f <span class="token operator">=</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">'/home/aistudio/dataset/test/test_pics.txt'</span><span class="token punctuation">,</span><span class="token string">'r'</span><span class="token punctuation">)</span>
filepath <span class="token operator">=</span> f<span class="token punctuation">.</span>readlines<span class="token punctuation">(</span><span class="token punctuation">)</span>
data <span class="token operator">=</span> <span class="token punctuation">[</span>filepath<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">" "</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>filepath<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">" "</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>filepath<span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">" "</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>filepath<span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">" "</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>filepath<span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">]</span><span class="token punctuation">.</span>split<span class="token punctuation">(</span><span class="token string">" "</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
f<span class="token punctuation">.</span>close<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>data<span class="token punctuation">)</span>
label_map <span class="token operator">=</span> dataset<span class="token punctuation">.</span>label_dict<span class="token punctuation">(</span><span class="token punctuation">)</span>
index <span class="token operator">=</span> <span class="token number">0</span>
run_states <span class="token operator">=</span> task<span class="token punctuation">.</span>predict<span class="token punctuation">(</span>data<span class="token operator">=</span>data<span class="token punctuation">)</span>
results <span class="token operator">=</span> <span class="token punctuation">[</span>run_state<span class="token punctuation">.</span>run_results <span class="token keyword">for</span> run_state <span class="token keyword">in</span> run_states<span class="token punctuation">]</span>
<span class="token keyword">for</span> batch_result <span class="token keyword">in</span> results<span class="token punctuation">:</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>batch_result<span class="token punctuation">)</span>
batch_result <span class="token operator">=</span> np<span class="token punctuation">.</span>argmax<span class="token punctuation">(</span>batch_result<span class="token punctuation">,</span> axis<span class="token operator">=</span><span class="token number">2</span><span class="token punctuation">)</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>batch_result<span class="token punctuation">)</span>
<span class="token keyword">for</span> result <span class="token keyword">in</span> batch_result<span class="token punctuation">:</span>
index <span class="token operator">+=</span> <span class="token number">1</span>
result <span class="token operator">=</span> label_map<span class="token punctuation">[</span>result<span class="token punctuation">]</span>
<span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"input %i is %s, and the predict result is %s"</span> <span class="token operator">%</span>
<span class="token punctuation">(</span>index<span class="token punctuation">,</span> data<span class="token punctuation">[</span>index <span class="token operator">-</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> result<span class="token punctuation">)</span><span class="token punctuation">)</span>
</code></pre>
<hr>
<p>代码到这里就结束了</p>
<h4>识别结果</h4>
<p><a href="http://img.e-com-net.com/image/info8/81469c7432d5457ca797942efc34efb6.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/81469c7432d5457ca797942efc34efb6.jpg" alt="百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别_第2张图片" width="650" height="351" style="border:1px solid black;"></a><br> 由结果可知，60%的正确率，太低了。<br> 可能是数据集的太小的缘故，也可能是参数配置不对。<br> 明天再调试。拜拜！</p>
<blockquote>
<p>写在后面：你只要在努力，就没有辜负时光<br> 三天前就已经开营，拖到现在才写一篇博客，是我太懒。</p>
</blockquote>
<hr>
<h3>隔天更新</h3>
<p>时隔一天后，模型终于能够识别这五张图，狂喜！<br> <a href="http://img.e-com-net.com/image/info8/be86dac7e21b4076a7630f25e2d371cd.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/be86dac7e21b4076a7630f25e2d371cd.jpg" alt="百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别_第3张图片" width="650" height="330" style="border:1px solid black;"></a>今天上午，我尝试过更改数据。把爬取的图片做了裁剪，见剩下演员的脸部。就像这样：<br> <a href="http://img.e-com-net.com/image/info8/a24f3dda11804e81aeaa0d98dce08459.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/a24f3dda11804e81aeaa0d98dce08459.jpg" alt="百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别_第4张图片" width="413" height="417" style="border:1px solid black;"></a><br> 就剩下个大脑袋，其余部分都裁剪了。<br> 实验发现：<br> 测试图片具有身体部分，因此脸部的特征不够突出，而训练用的图片基本都是脸部特征，训练出来的模型识别率极低。</p>
<h4>策略配置</h4>
<p>这是最后模型微调前的策略配置：<br> <a href="http://img.e-com-net.com/image/info8/ad1eb4e010104eb5b855559eaad3a0a1.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/ad1eb4e010104eb5b855559eaad3a0a1.jpg" alt="百度AIStudio-Python小白逆袭大神-Day4-PaddleHub之《青春有你2》作业：五人识别_第5张图片" width="650" height="122" style="border:1px solid black;"></a>昨天配置的策略轮数是：</p>
<pre><code class="prism language-python">num_epoch <span class="token operator">=</span> <span class="token number">5</span>
</code></pre>
<p>昨天模型应该是过拟合。</p>
<hr>
<p>下面是本地图片爬取与图像数据增强</p>
<blockquote>
<p>点击这里</p>
</blockquote>
</div>
</div>
</div>
</div>
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<div>《投行人生》----作者詹姆斯-A-朗德摩根斯坦利副主席40年的职业洞见-很短小精悍的篇幅，比较适合初入职场的新人。第一部分成功的职业生涯需要规划1.情商归为适应能力分享与协作同理心适应能力，更多的是自我意识，你有能力识别自己的情并分辨这些情绪如何影响你的思想和行为。2.对于初入职场的人的建议，细节，截止日期和数据很重要截止日期，一种有效的方法是请老板为你所有的任务进行优先级排序。和老板喝咖啡的好</div>
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<li><a href="/article/1835509898507546624.htm"
title="《策划经理回忆录之二》" target="_blank">《策划经理回忆录之二》</a>
<span class="text-muted">路基雅虎</span>
<div>话说三年变六年，飘了，飘了……眨眼，2013年5月，老吴回到了他的家乡——油城从新开启他的工作幻想症生涯。很庆幸，这是一家很有追求，同时敢于尝试的，且实力不容低调的新星房企——金源置业(前身泰源置业)更值得庆幸的是第一个盘就是油城十路的标杆之一:金源盛世。2013年5月，到2015年11月，两年的陪伴，迎来了一场大爆发。2000个筹，5万/筹，直接回笼1个亿！！！这……让我开始认真审视这座看似五线</div>
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title="Long类型前后端数据不一致" target="_blank">Long类型前后端数据不一致</a>
<span class="text-muted">igotyback</span>
<a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF/1.htm">前端</a>
<div>响应给前端的数据浏览器控制台中response中看到的Long类型的数据是正常的到前端数据不一致前后端数据类型不匹配是一个常见问题，尤其是当后端使用Java的Long类型（64位）与前端JavaScript的Number类型（最大安全整数为2^53-1，即16位）进行数据交互时，很容易出现精度丢失的问题。这是因为JavaScript中的Number类型无法安全地表示超过16位的整数。为了解决这个问</div>
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<li><a href="/article/1835509770287673344.htm"
title="swagger访问路径" target="_blank">swagger访问路径</a>
<span class="text-muted">igotyback</span>
<a class="tag" taget="_blank" href="/search/swagger/1.htm">swagger</a>
<div>Swagger2.x版本访问地址：http://{ip}:{port}/{context-path}/swagger-ui.html{ip}是你的服务器IP地址。{port}是你的应用服务端口，通常为8080。{context-path}是你的应用上下文路径，如果应用部署在根路径下，则为空。Swagger3.x版本对于Swagger3.x版本（也称为OpenAPI3）访问地址：http://{ip</div>
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title="如何在 Fork 的 GitHub 项目中保留自己的修改并同步上游更新？github_fork_update" target="_blank">如何在 Fork 的 GitHub 项目中保留自己的修改并同步上游更新？github_fork_update</a>
<span class="text-muted">iBaoxing</span>
<a class="tag" taget="_blank" href="/search/github/1.htm">github</a>
<div>如何在Fork的GitHub项目中保留自己的修改并同步上游更新？在GitHub上Fork了一个项目后，你可能会对项目进行一些修改，同时原作者也在不断更新。如果想要在保留自己修改的基础上，同步原作者的最新更新，很多人会不知所措。本文将详细讲解如何在不丢失自己改动的情况下，将上游仓库的更新合并到自己的仓库中。问题描述假设你在GitHub上Fork了一个项目，并基于该项目做了一些修改，随后你发现原作者对</div>
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title="扫地机类清洁产品之直流无刷电机控制" target="_blank">扫地机类清洁产品之直流无刷电机控制</a>
<span class="text-muted">悟空胆好小</span>
<a class="tag" taget="_blank" href="/search/%E6%B8%85%E6%B4%81%E6%9C%8D%E5%8A%A1%E6%9C%BA%E5%99%A8%E4%BA%BA/1.htm">清洁服务机器人</a><a class="tag" taget="_blank" href="/search/%E5%8D%95%E7%89%87%E6%9C%BA/1.htm">单片机</a><a class="tag" taget="_blank" href="/search/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/1.htm">人工智能</a>
<div>扫地机类清洁产品之直流无刷电机控制1.1前言扫地机产品有很多的电机控制，滚刷电机1个，边刷电机1-2个，清水泵电机，风机一个，部分中高端产品支持抹布功能，也就是存在抹布盘电机，还有追觅科沃斯石头等边刷抬升电机，滚刷抬升电机等的，这些电机有直流有刷电机，直接无刷电机，步进电机，电磁阀，挪动泵等不同类型。电机的原理，驱动控制方式也不行。接下来一段时间的几个文章会作个专题分析分享。直流有刷电机会自动持续</div>
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title="Linux下QT开发的动态库界面弹出操作（SDL2）" target="_blank">Linux下QT开发的动态库界面弹出操作（SDL2）</a>
<span class="text-muted">13jjyao</span>
<a class="tag" taget="_blank" href="/search/QT%E7%B1%BB/1.htm">QT类</a><a class="tag" taget="_blank" href="/search/qt/1.htm">qt</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a><a class="tag" taget="_blank" href="/search/sdl2/1.htm">sdl2</a><a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a>
<div>需求：操作系统为linux，开发框架为qt，做成需带界面的qt动态库，调用方为java等非qt程序难点：调用方为java等非qt程序，也就是说调用方肯定不带QApplication::exec()，缺少了这个，QTimer等事件和QT创建的窗口将不能弹出(包括opencv也是不能弹出)；这与qt调用本身qt库是有本质的区别的思路：1.调用方缺QApplication::exec()，那么我们在接口</div>
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title="店群合一模式下的社区团购新发展——结合链动 2+1 模式、AI 智能名片与 S2B2C 商城小程序源码" target="_blank">店群合一模式下的社区团购新发展——结合链动 2+1 模式、AI 智能名片与 S2B2C 商城小程序源码</a>
<span class="text-muted">说私域</span>
<a class="tag" taget="_blank" href="/search/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/1.htm">人工智能</a><a class="tag" taget="_blank" href="/search/%E5%B0%8F%E7%A8%8B%E5%BA%8F/1.htm">小程序</a>
<div>摘要：本文探讨了店群合一的社区团购平台在当今商业环境中的重要性和优势。通过分析店群合一模式如何将互联网社群与线下终端紧密结合，阐述了链动2+1模式、AI智能名片和S2B2C商城小程序源码在这一模式中的应用价值。这些创新元素的结合为社区团购带来了新的机遇，提升了用户信任感、拓展了营销渠道，并实现了线上线下的完美融合。一、引言随着互联网技术的不断发展，社区团购作为一种新兴的商业模式，在满足消费者日常需</div>
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<li><a href="/article/1835508630959517696.htm"
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<span class="text-muted">陈陈_19b4</span>
<div>10月27日，阴。阅读书目:《次第花开》。作者:希阿荣博堪布，是当今藏传佛家宁玛派最伟大的上师法王，如意宝晋美彭措仁波切颇具影响力的弟子之一。多年以来，赴海内外各地弘扬佛法，以正式授课、现场开示、发表文章等多种方法指导佛学弟子修行佛法。代表作《寂静之道》、《生命这出戏》、《透过佛法看世界》自出版以来一直是佛教类书籍中的畅销书。图片发自App金句:1.佛陀说，一切痛苦的根源在于我们长期以来对自身及外</div>
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title="2021-08-26" target="_blank">2021-08-26</a>
<span class="text-muted">影幽</span>
<div>在生活中，女人与男人的感悟往往有所不同。人生最大的舞台就是生活，大幕随时都可能拉开，关键是你愿不愿意表演都无法躲避。在生活中，遇事不要急躁，不要急于下结论，尤其生气时不要做决断，要学会换位思考，大事化小小事化了，把复杂的事情尽量简单处理，千万不要把简单的事情复杂化。永远不要扭曲，别人善意，无药可救。昨天是张过期的支票，明天是张信用卡，只有今天才是现金，要善加利用！执着的攀登者不必去与别人比较自己的</div>
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title="消息中间件有哪些常见类型" target="_blank">消息中间件有哪些常见类型</a>
<span class="text-muted">xmh-sxh-1314</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a>
<div>消息中间件根据其设计理念和用途，可以大致分为以下几种常见类型：点对点消息队列（Point-to-PointMessagingQueues）：在这种模型中，消息被发送到特定的队列中，消费者从队列中取出并处理消息。队列中的消息只能被一个消费者消费，消费后即被删除。常见的实现包括IBM的MQSeries、RabbitMQ的部分使用场景等。适用于任务分发、负载均衡等场景。发布/订阅消息模型（Pub/Sub</div>
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title="怎么样才能成为专业的程序员？" target="_blank">怎么样才能成为专业的程序员？</a>
<span class="text-muted">cocos2d-x小菜</span>
<a class="tag" taget="_blank" href="/search/%E7%BC%96%E7%A8%8B/1.htm">编程</a><a class="tag" taget="_blank" href="/search/PHP/1.htm">PHP</a>
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如何要想成为一名专业的程序员？仅仅会写代码是不够的。从团队合作去解决问题到版本控制，你还得具备其他关键技能的工具包。当我们询问相关的专业开发人员，那些必备的关键技能都是什么的时候，下面是我们了解到的情况。
关于如何学习代码，各种声音很多，然后很多人就被误导为成为专业开发人员懂得一门编程语言就够了？！呵呵，就像其他工作一样，光会一个技能那是远远不够的。如果你想要成为</div>
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title="java web开发 高并发处理" target="_blank">java web开发 高并发处理</a>
<span class="text-muted">BreakingBad</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/Web/1.htm">Web</a><a class="tag" taget="_blank" href="/search/%E5%B9%B6%E5%8F%91/1.htm">并发</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91/1.htm">开发</a><a class="tag" taget="_blank" href="/search/%E5%A4%84%E7%90%86/1.htm">处理</a><a class="tag" taget="_blank" href="/search/%E9%AB%98/1.htm">高</a>
<div>java处理高并发高负载类网站中数据库的设计方法（java教程,java处理大量数据，java高负载数据） 一：高并发高负载类网站关注点之数据库 没错,首先是数据库,这是大多数应用所面临的首个SPOF。尤其是Web2.0的应用，数据库的响应是首先要解决的。 一般来说MySQL是最常用的，可能最初是一个mysql主机，当数据增加到100万以上，那么，MySQL的效能急剧下降。常用的优化措施是M-S（</div>
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<li><a href="/article/350.htm"
title="mysql批量更新" target="_blank">mysql批量更新</a>
<span class="text-muted">ekian</span>
<a class="tag" taget="_blank" href="/search/mysql/1.htm">mysql</a>
<div>mysql更新优化：
一版的更新的话都是采用update set的方式，但是如果需要批量更新的话，只能for循环的执行更新。或者采用executeBatch的方式，执行更新。无论哪种方式，性能都不见得多好。
三千多条的更新，需要3分多钟。
查询了批量更新的优化，有说replace into的方式，即：
replace into tableName(id,status) values</div>
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<li><a href="/article/477.htm"
title="微软BI（3）" target="_blank">微软BI（3）</a>
<span class="text-muted">18289753290</span>
<a class="tag" taget="_blank" href="/search/%E5%BE%AE%E8%BD%AFBI+SSIS/1.htm">微软BI SSIS</a>
<div>1)
Q：该列违反了完整性约束错误；已获得 OLE DB 记录。源:“Microsoft SQL Server Native Client 11.0” Hresult: 0x80004005 说明:“不能将值 NULL 插入列 'FZCHID'，表 'JRB_EnterpriseCredit.dbo.QYFZCH'；列不允许有 Null 值。INSERT 失败。”。
A：一般这类问题的存在是 </div>
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<span class="text-muted">g21121</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a>
<div> List是一个有序的 collection（也称为序列）。此接口的用户可以对列表中每个元素的插入位置进行精确地控制。用户可以根据元素的整数索引（在列表中的位置）访问元素，并搜索列表中的元素。
与 set 不同，列表通常允许重复</div>
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<span class="text-muted">永夜-极光</span>
<a class="tag" taget="_blank" href="/search/%E8%AF%BB%E4%B9%A6%E7%AC%94%E8%AE%B0/1.htm">读书笔记</a>
<div> 1. K是一家加工厂,需要采购原材料,有A,B,C,D 4家供应商,其中A给出的价格最低,性价比最高,那么假如你是这家企业的采购经理,你会如何决策?
传统决策: A:100%订单 B,C,D:0%
&nbs</div>
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title="centos 安装 Codeblocks" target="_blank">centos 安装 Codeblocks</a>
<span class="text-muted">随便小屋</span>
<a class="tag" taget="_blank" href="/search/codeblocks/1.htm">codeblocks</a>
<div>1.安装gcc,需要c和c++两部分,默认安装下,CentOS不安装编译器的,在终端输入以下命令即可yum install gccyum install gcc-c++
2.安装gtk2-devel,因为默认已经安装了正式产品需要的支持库,但是没有安装开发所需要的文档.yum install gtk2*
3. 安装wxGTK
yum search w</div>
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<a class="tag" taget="_blank" href="/search/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/1.htm">设计模式</a>
<div>1、ABSTRACT FACTORY—追MM少不了请吃饭了，麦当劳的鸡翅和肯德基的鸡翅都是MM爱吃的东西，虽然口味有所不同，但不管你带MM去麦当劳或肯德基，只管向服务员说“来四个鸡翅”就行了。麦当劳和肯德基就是生产鸡翅的Factory 工厂模式：客户类和工厂类分开。消费者任何时候需要某种产品，只需向工厂请求即可。消费者无须修改就可以接纳新产品。缺点是当产品修改时，工厂类也要做相应的修改。如：</div>
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<span class="text-muted">aoyouzi</span>
<a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E7%AE%A1%E7%90%86+CheckLists/1.htm">开发管理 CheckLists</a>
<div>开发管理 CheckLists(23) -使项目组度过完整的生命周期
开发管理 CheckLists(22) -组织项目资源
开发管理 CheckLists(21) -控制项目的范围开发管理 CheckLists(20) -项目利益相关者责任开发管理 CheckLists(19) -选择合适的团队成员开发管理 CheckLists(18) -敏捷开发 Scrum Master 工作开发管理 C</div>
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<li><a href="/article/1239.htm"
title="js实现切换" target="_blank">js实现切换</a>
<span class="text-muted">百合不是茶</span>
<a class="tag" taget="_blank" href="/search/JavaScript/1.htm">JavaScript</a><a class="tag" taget="_blank" href="/search/%E6%A0%8F%E7%9B%AE%E5%88%87%E6%8D%A2/1.htm">栏目切换</a>
<div>js主要功能之一就是实现页面的特效,窗体的切换可以减少页面的大小,被门户网站大量应用思路:
1,先将要显示的设置为display:bisible 否则设为none
2,设置栏目的id ,js获取栏目的id,如果id为Null就设置为显示
3,判断js获取的id名字;再设置是否显示
代码实现:
html代码:
<di</div>
</li>
<li><a href="/article/1366.htm"
title="周鸿祎在360新员工入职培训上的讲话" target="_blank">周鸿祎在360新员工入职培训上的讲话</a>
<span class="text-muted">bijian1013</span>
<a class="tag" taget="_blank" href="/search/%E6%84%9F%E6%82%9F/1.htm">感悟</a><a class="tag" taget="_blank" href="/search/%E9%A1%B9%E7%9B%AE%E7%AE%A1%E7%90%86/1.htm">项目管理</a><a class="tag" taget="_blank" href="/search/%E4%BA%BA%E7%94%9F/1.htm">人生</a><a class="tag" taget="_blank" href="/search/%E8%81%8C%E5%9C%BA/1.htm">职场</a>
<div> 这篇文章也是最近偶尔看到的，考虑到原博客发布者可能将其删除等原因，也更方便个人查找，特将原文拷贝再发布的。“学东西是为自己的，不要整天以混的姿态来跟公司博弈，就算是混，我觉得你要是能在混的时间里，收获一些别的有利于人生发展的东西，也是不错的，看你怎么把握了”，看了之后，对这句话记忆犹新。 &</div>
</li>
<li><a href="/article/1493.htm"
title="前端Web开发的页面效果" target="_blank">前端Web开发的页面效果</a>
<span class="text-muted">Bill_chen</span>
<a class="tag" taget="_blank" href="/search/html/1.htm">html</a><a class="tag" taget="_blank" href="/search/Web/1.htm">Web</a><a class="tag" taget="_blank" href="/search/Microsoft/1.htm">Microsoft</a>
<div>1.IE6下png图片的透明显示：
<img src="图片地址" border="0" style="Filter.Alpha(Opacity)=数值(100),style=数值(3)"/>
或在<head></head>间加一段JS代码让透明png图片正常显示。
2.<li>标</div>
</li>
<li><a href="/article/1620.htm"
title="【JVM五】老年代垃圾回收：并发标记清理GC(CMS GC)" target="_blank">【JVM五】老年代垃圾回收：并发标记清理GC(CMS GC)</a>
<span class="text-muted">bit1129</span>
<a class="tag" taget="_blank" href="/search/%E5%9E%83%E5%9C%BE%E5%9B%9E%E6%94%B6/1.htm">垃圾回收</a>
<div> CMS概述
并发标记清理垃圾回收(Concurrent Mark and Sweep GC）算法的主要目标是在GC过程中，减少暂停用户线程的次数以及在不得不暂停用户线程的请夸功能，尽可能短的暂停用户线程的时间。这对于交互式应用，比如web应用来说，是非常重要的。
CMS垃圾回收针对新生代和老年代采用不同的策略。相比同吞吐量垃圾回收，它要复杂的多。吞吐量垃圾回收在执</div>
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<li><a href="/article/1747.htm"
title="Struts2技术总结" target="_blank">Struts2技术总结</a>
<span class="text-muted">白糖_</span>
<a class="tag" taget="_blank" href="/search/struts2/1.htm">struts2</a>
<div>
必备jar文件
早在struts2.0.*的时候，struts2的必备jar包需要如下几个：
commons-logging-*.jar Apache旗下commons项目的log日志包
freemarker-*.jar </div>
</li>
<li><a href="/article/1874.htm"
title="Jquery easyui layout应用注意事项" target="_blank">Jquery easyui layout应用注意事项</a>
<span class="text-muted">bozch</span>
<a class="tag" taget="_blank" href="/search/jquery/1.htm">jquery</a><a class="tag" taget="_blank" href="/search/%E6%B5%8F%E8%A7%88%E5%99%A8/1.htm">浏览器</a><a class="tag" taget="_blank" href="/search/easyui/1.htm">easyui</a><a class="tag" taget="_blank" href="/search/layout/1.htm">layout</a>
<div>在jquery easyui中提供了easyui-layout布局，他的布局比较局限，类似java中GUI的border布局。下面对其使用注意事项作简要介绍：
如果在现有的工程中前台界面均应用了jquery easyui，那么在布局的时候最好应用jquery eaysui的layout布局，否则在表单页面（编辑、查看、添加等等）在不同的浏览器会出</div>
</li>
<li><a href="/article/2001.htm"
title="java-拷贝特殊链表：有一个特殊的链表，其中每个节点不但有指向下一个节点的指针pNext，还有一个指向链表中任意节点的指针pRand，如何拷贝这个特殊链表？" target="_blank">java-拷贝特殊链表：有一个特殊的链表，其中每个节点不但有指向下一个节点的指针pNext，还有一个指向链表中任意节点的指针pRand，如何拷贝这个特殊链表？</a>
<span class="text-muted">bylijinnan</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a>
<div>
public class CopySpecialLinkedList {
/**
* 题目：有一个特殊的链表，其中每个节点不但有指向下一个节点的指针pNext，还有一个指向链表中任意节点的指针pRand，如何拷贝这个特殊链表？
拷贝pNext指针非常容易，所以题目的难点是如何拷贝pRand指针。
假设原来链表为A1 -> A2 ->... -> An，新拷贝</div>
</li>
<li><a href="/article/2128.htm"
title="color" target="_blank">color</a>
<span class="text-muted">Chen.H</span>
<a class="tag" taget="_blank" href="/search/JavaScript/1.htm">JavaScript</a><a class="tag" taget="_blank" href="/search/html/1.htm">html</a><a class="tag" taget="_blank" href="/search/css/1.htm">css</a>
<div><!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> <HTML> <HEAD>&nbs</div>
</li>
<li><a href="/article/2255.htm"
title="[信息与战争]移动通讯与网络" target="_blank">[信息与战争]移动通讯与网络</a>
<span class="text-muted">comsci</span>
<a class="tag" taget="_blank" href="/search/%E7%BD%91%E7%BB%9C/1.htm">网络</a>
<div> 两个坚持:手机的电池必须可以取下来
光纤不能够入户,只能够到楼宇
建议大家找这本书看看:<&</div>
</li>
<li><a href="/article/2382.htm"
title="oracle flashback query(闪回查询)" target="_blank">oracle flashback query(闪回查询)</a>
<span class="text-muted">daizj</span>
<a class="tag" taget="_blank" href="/search/oracle/1.htm">oracle</a><a class="tag" taget="_blank" href="/search/flashback+query/1.htm">flashback query</a><a class="tag" taget="_blank" href="/search/flashback+table/1.htm">flashback table</a>
<div>在Oracle 10g中，Flash back家族分为以下成员：
Flashback Database
Flashback Drop
Flashback Table
Flashback Query(分Flashback Query,Flashback Version Query，Flashback Transaction Query)
下面介绍一下Flashback Drop 和Flas</div>
</li>
<li><a href="/article/2509.htm"
title="zeus持久层DAO单元测试" target="_blank">zeus持久层DAO单元测试</a>
<span class="text-muted">deng520159</span>
<a class="tag" taget="_blank" href="/search/%E5%8D%95%E5%85%83%E6%B5%8B%E8%AF%95/1.htm">单元测试</a>
<div>zeus代码测试正紧张进行中,但由于工作比较忙,但速度比较慢.现在已经完成读写分离单元测试了,现在把几种情况单元测试的例子发出来,希望有人能进出意见,让它走下去.
本文是zeus的dao单元测试:
1.单元测试直接上代码
package com.dengliang.zeus.webdemo.test;
import org.junit.Test;
import o</div>
</li>
<li><a href="/article/2636.htm"
title="C语言学习三printf函数和scanf函数学习" target="_blank">C语言学习三printf函数和scanf函数学习</a>
<span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/c/1.htm">c</a><a class="tag" taget="_blank" href="/search/printf/1.htm">printf</a><a class="tag" taget="_blank" href="/search/scanf/1.htm">scanf</a><a class="tag" taget="_blank" href="/search/language/1.htm">language</a>
<div>printf函数
/*
2013年3月10日20:42:32
地点：北京潘家园
功能：
目的：
测试%x %X %#x %#X的用法
*/
# include <stdio.h>
int main(void)
{
printf("哈哈！\n"); // \n表示换行
int i = 10;
printf</div>
</li>
<li><a href="/article/2763.htm"
title="那你为什么小时候不好好读书?" target="_blank">那你为什么小时候不好好读书?</a>
<span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/life/1.htm">life</a>
<div>dady, 我今天捡到了十块钱, 不过我还给那个人了
good girl! 那个人有没有和你讲thank you啊
没有啦....他拉我的耳朵我才把钱还给他的, 他哪里会和我讲thank you
爸爸, 如果地上有一张5块一张10块你拿哪一张呢....
当然是拿十块的咯...
爸爸你很笨的, 你不会两张都拿
爸爸为什么上个月那个人来跟你讨钱, 你告诉他没</div>
</li>
<li><a href="/article/2890.htm"
title="iptables开放端口" target="_blank">iptables开放端口</a>
<span class="text-muted">Fanyucai</span>
<a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a><a class="tag" taget="_blank" href="/search/iptables/1.htm">iptables</a><a class="tag" taget="_blank" href="/search/%E7%AB%AF%E5%8F%A3/1.htm">端口</a>
<div>1，找到配置文件
vi /etc/sysconfig/iptables
2，添加端口开放，增加一行，开放18081端口
-A INPUT -m state --state NEW -m tcp -p tcp --dport 18081 -j ACCEPT
3，保存
ESC
:wq!
4，重启服务
service iptables </div>
</li>
<li><a href="/article/3017.htm"
title="Ehcache（05）——缓存的查询" target="_blank">Ehcache（05）——缓存的查询</a>
<span class="text-muted">234390216</span>
<a class="tag" taget="_blank" href="/search/%E6%8E%92%E5%BA%8F/1.htm">排序</a><a class="tag" taget="_blank" href="/search/ehcache/1.htm">ehcache</a><a class="tag" taget="_blank" href="/search/%E7%BB%9F%E8%AE%A1/1.htm">统计</a><a class="tag" taget="_blank" href="/search/query/1.htm">query</a>
<div>缓存的查询
目录
1. 使Cache可查询
1.1 基于Xml配置
1.2 基于代码的配置
2 指定可搜索的属性
2.1 可查询属性类型
2.2 &</div>
</li>
<li><a href="/article/3144.htm"
title="通过hashset找到数组中重复的元素" target="_blank">通过hashset找到数组中重复的元素</a>
<span class="text-muted">jackyrong</span>
<a class="tag" taget="_blank" href="/search/hashset/1.htm">hashset</a>
<div> 如何在hashset中快速找到重复的元素呢?方法很多，下面是其中一个办法：
int[] array = {1,1,2,3,4,5,6,7,8,8};
Set<Integer> set = new HashSet<Integer>();
for(int i = 0</div>
</li>
<li><a href="/article/3271.htm"
title="使用ajax和window.history.pushState无刷新改变页面内容和地址栏URL" target="_blank">使用ajax和window.history.pushState无刷新改变页面内容和地址栏URL</a>
<span class="text-muted">lanrikey</span>
<a class="tag" taget="_blank" href="/search/history/1.htm">history</a>
<div>后退时关闭当前页面
<script type="text/javascript">
jQuery(document).ready(function ($) {
if (window.history && window.history.pushState) {</div>
</li>
<li><a href="/article/3398.htm"
title="应用程序的通信成本" target="_blank">应用程序的通信成本</a>
<span class="text-muted">netkiller.github.com</span>
<a class="tag" taget="_blank" href="/search/%E8%99%9A%E6%8B%9F%E6%9C%BA/1.htm">虚拟机</a><a class="tag" taget="_blank" href="/search/%E5%BA%94%E7%94%A8%E6%9C%8D%E5%8A%A1%E5%99%A8/1.htm">应用服务器</a><a class="tag" taget="_blank" href="/search/%E9%99%88%E6%99%AF%E5%B3%B0/1.htm">陈景峰</a><a class="tag" taget="_blank" href="/search/netkiller/1.htm">netkiller</a><a class="tag" taget="_blank" href="/search/neo/1.htm">neo</a>
<div>应用程序的通信成本
什么是通信
一个程序中两个以上功能相互传递信号或数据叫做通信。
什么是成本
这是是指时间成本与空间成本。 时间就是传递数据所花费的时间。空间是指传递过程耗费容量大小。
都有哪些通信方式
全局变量
线程间通信
共享内存
共享文件
管道
Socket
硬件（串口，USB） 等等
全局变量
全局变量是成本最低通信方法，通过设置</div>
</li>
<li><a href="/article/3525.htm"
title="一维数组与二维数组的声明与定义" target="_blank">一维数组与二维数组的声明与定义</a>
<span class="text-muted">恋洁e生</span>
<a class="tag" taget="_blank" href="/search/%E4%BA%8C%E7%BB%B4%E6%95%B0%E7%BB%84/1.htm">二维数组</a><a class="tag" taget="_blank" href="/search/%E4%B8%80%E7%BB%B4%E6%95%B0%E7%BB%84/1.htm">一维数组</a><a class="tag" taget="_blank" href="/search/%E5%AE%9A%E4%B9%89/1.htm">定义</a><a class="tag" taget="_blank" href="/search/%E5%A3%B0%E6%98%8E/1.htm">声明</a><a class="tag" taget="_blank" href="/search/%E5%88%9D%E5%A7%8B%E5%8C%96/1.htm">初始化</a>
<div>/** * */ package test20111005; /** * @author FlyingFire * @date:2011-11-18 上午04:33:36 * @author ：代码整理 * @introduce :一维数组与二维数组的初始化 *summary： */ public c</div>
</li>
<li><a href="/article/3652.htm"
title="Spring Mybatis独立事务配置" target="_blank">Spring Mybatis独立事务配置</a>
<span class="text-muted">toknowme</span>
<a class="tag" taget="_blank" href="/search/mybatis/1.htm">mybatis</a>
<div>在项目中有很多地方会使用到独立事务，下面以获取主键为例
（1）修改配置文件spring-mybatis.xml <!-- 开启事务支持 --> <tx:annotation-driven transaction-manager="transactionManager" /> &n</div>
</li>
<li><a href="/article/3779.htm"
title="更新Anadroid SDK Tooks之后，Eclipse提示No update were found" target="_blank">更新Anadroid SDK Tooks之后，Eclipse提示No update were found</a>
<span class="text-muted">xp9802</span>
<a class="tag" taget="_blank" href="/search/eclipse/1.htm">eclipse</a>
<div>使用Android SDK Manager 更新了Anadroid SDK Tooks 之后，
打开eclipse提示 This Android SDK requires Android Developer Toolkit version 23.0.0 or above, 点击Check for Updates
检测一会后提示 No update were found </div>
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