:
python计算机毕业设计 微信机器人
资源介绍:
python计算机毕业设计 微信机器人
"# joke-weather-wechat"
"# joke-weather-wechat"
资源文件列表:
python计算机毕业设计 微信机器人.zip 大约有82个文件
wechat_robot/
wechat_robot/joke.py 748B
- wechat_robot/config.py 363B
- wechat_robot/requirements.txt 46B
- wechat_robot/weather.py 1.26KB
- wechat_robot/test.py 184B
- wechat_robot/README.md 52B
- wechat_robot/.git/
- wechat_robot/.git/config 311B
- wechat_robot/.git/objects/
- wechat_robot/.git/objects/57/
- wechat_robot/.git/objects/57/892f0cf8a0b55bb2426930eeb405002788cb0c 354B
- wechat_robot/.git/objects/6f/
- wechat_robot/.git/objects/6f/8212989beb6d8b96224fa7d64447a4aa8886a9 58B
- wechat_robot/.git/objects/04/
- wechat_robot/.git/objects/04/d7d51f59b5e89910246786ae8fa367a0873eee 446B
- wechat_robot/.git/objects/67/
- wechat_robot/.git/objects/67/e970716c57de43a5f2a282e0cc7229a00a06c2 733B
- wechat_robot/.git/objects/94/
- wechat_robot/.git/objects/94/fe3aed0ce921c05d6f995696c1561f02ee913e 165B
- wechat_robot/.git/objects/ac/
- wechat_robot/.git/objects/ac/6e93c7f1bf6acb3c2a3ee76c1caf26cc406ff5 63B
- wechat_robot/.git/objects/d0/
- wechat_robot/.git/objects/d0/f17fac2e941c6957d10826ae8223b38735a011 153B
- wechat_robot/.git/objects/c0/
- wechat_robot/.git/objects/c0/a4d31c9f524a691dee8edfb7d725d9bb36b41e 41B
- wechat_robot/.git/objects/20/
- wechat_robot/.git/objects/20/adb145b83910ee8d81f31428fca38bdf411a4c 354B
- wechat_robot/.git/objects/pack/
- wechat_robot/.git/objects/74/
- wechat_robot/.git/objects/74/e76605b43aa9f7a756df74fe986028f252dc39 122B
- wechat_robot/.git/objects/31/
- wechat_robot/.git/objects/31/34adc1070371d66bc3026dccc0f8bd5b38066c 1.16KB
- wechat_robot/.git/objects/info/
- wechat_robot/.git/objects/98/
- wechat_robot/.git/objects/98/2731a62eb6fb39985839921d5b508960629bd1 54B
- wechat_robot/.git/objects/6d/
- wechat_robot/.git/objects/6d/952df2975257b98e582b4564049c25e9f18596 453B
- wechat_robot/.git/objects/70/
- wechat_robot/.git/objects/70/853f98e6356b172aaa92727562d4a4c4e2c8fc 150B
- wechat_robot/.git/objects/8d/
- wechat_robot/.git/objects/8d/7d2cbfedae7c131886f159792612e4f72512f0 59B
- wechat_robot/.git/objects/2e/
- wechat_robot/.git/objects/2e/22a63570081dc5b3259b97239bf80e603487ae 57B
- wechat_robot/.git/HEAD 23B
- wechat_robot/.git/info/
- wechat_robot/.git/info/exclude 240B
- wechat_robot/.git/logs/
- wechat_robot/.git/logs/HEAD 457B
- wechat_robot/.git/logs/refs/
- wechat_robot/.git/logs/refs/heads/
- wechat_robot/.git/logs/refs/heads/master 457B
- wechat_robot/.git/logs/refs/remotes/
- wechat_robot/.git/logs/refs/remotes/origin/
- wechat_robot/.git/logs/refs/remotes/origin/master 414B
- wechat_robot/.git/description 73B
- wechat_robot/.git/hooks/
- wechat_robot/.git/hooks/commit-msg.sample 896B
- wechat_robot/.git/hooks/pre-rebase.sample 4.78KB
- wechat_robot/.git/hooks/pre-commit.sample 1.6KB
- wechat_robot/.git/hooks/applypatch-msg.sample 478B
- wechat_robot/.git/hooks/fsmonitor-watchman.sample 3.25KB
- wechat_robot/.git/hooks/pre-receive.sample 544B
- wechat_robot/.git/hooks/prepare-commit-msg.sample 1.46KB
- wechat_robot/.git/hooks/post-update.sample 189B
- wechat_robot/.git/hooks/pre-applypatch.sample 424B
- wechat_robot/.git/hooks/pre-push.sample 1.32KB
- wechat_robot/.git/hooks/update.sample 3.53KB
- wechat_robot/.git/refs/
- wechat_robot/.git/refs/heads/
- wechat_robot/.git/refs/heads/master 41B
- wechat_robot/.git/refs/tags/
- wechat_robot/.git/refs/remotes/
- wechat_robot/.git/refs/remotes/origin/
- wechat_robot/.git/refs/remotes/origin/master 41B
- wechat_robot/.git/index 834B
- wechat_robot/.git/COMMIT_EDITMSG 29B
- wechat_robot/main.py 659B
- wechat_robot/wechat_robot.py 2.79KB
- 微信机器人程序使用说明.doc 363.5KB
- 程序配置说明.docx 20.12KB
- 资源使用须知.pdf 57.45KB
python 计算机毕业程序设计 今天吃什么代码.
基于swing考试管理系统
开发框架：eclipse + java + jdk + mysql
数据库：mysql
不能有中文路径
角色介绍
管理员
admins
123456
学生用户
qqq
123456
模块介绍
管理员
登录模块
考题管理
用户管理
考试管理
退出系统
学生用户
登录模块
注册模块
考试系统
查看成绩
密码修改
关于系统
退出系统
数据库介绍
数据库名称 db_exam
tb_grade
id userName
在MATLAB中进行水果草莓检测，可以采用图像处理技术和机器学习算法来实现。
首先，可以使用图像处理技术来提取草莓的特征。可以尝试以下步骤：
1. 读取原始图像并将其转换为HSV颜色空间。HSV颜色空间可以更好地区分草莓的颜色。
2. 使用颜色阈值将图像转换为二值图像。可以通过调整阈值来选择草莓的颜色范围。
3. 对二值图像进行形态学操作，如腐蚀和膨胀，以去除噪声或填充空洞。
4. 使用区域标记算法找到图像中的草莓区域。可以使用连通组件分析或边界跟踪算法来实现。
5. 根据草莓区域的大小、形状或其他特征，可以进一步筛选出可能是草莓的区域。
接下来，可以使用机器学习算法来训练一个分类器来识别草莓。可以采用以下步骤：
1. 为了训练分类器，需要准备一组标记好的草莓图像和非草莓图像。可以从图像库中手动标记图像，或者使用现有的草莓图像数据库。
2. 提取草莓和非草莓图像的特征。可以使用颜色直方图、形状、纹理等特征。
3. 使用草莓和非草莓图像的特征作为输入，标记作为输出，来训练分类器。常用的分类器包括支持向量机、决策树、随机森林等。
4. 使用训练好的分类器来对新的图像进
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