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README
MulanPSL-2.0

这是来自绵阳师范学院信息工程学院17级5班汤琳老师物联网工程课程的课堂作业.

成员:
组长:陈云
组员:崔向,黄宏毅,李典耘,蒋志勤

比赛需要故只开源了粗劣的第一个版本demo实现,第二版本改进使用yoloV3模型进行垃圾分类检测,机器臂分拣垃圾,垃圾分类数据集重新收集,并有微信小程序的用户查询垃圾分类及反馈机制 注意看ReadMe文件,注意看ReadMe文件,注意看ReadMe文件

B站视频介绍地址:https://www.bilibili.com/video/av80830870

交流群:1074171553

题主双非师范院校2021考研狗,如果你觉得这个小项目有帮助到你,请为项目点一个star,不管是考试型选手毕设项目被迫营业还是直接拿去二开参加比赛,这些都没问题,开源项目就是人人为我我为人人,但请尊重他人劳动成果,大家都是同龄人.心上无垢,林间有风.

材料清单

  1. 树莓派 1个

  2. pca9685 16路舵机驱动板 1个

  3. 7寸可触摸显示屏一个

  4. MG996R 舵机4个

  5. 垃圾桶4个

  6. usb免驱动摄像头1个

  7. 树莓派GPIO扩展板转接线柱1个

  8. 硅胶航模导线若干

环境需求

1.开发环境

神经网络搭建—python 依赖 tensorflow,keras

训练图片来源华为云2019垃圾分类大赛提供

训练图片地址:https://developer.huaweicloud.com/hero/forum.php?mod=viewthread&tid=24106

下载图片文件后将文件解压覆盖为 garbage_classify 放入 垃圾分类-本地训练/根目录

神经网络开源模型--- resnet50

models 目录需要手动下载resnet50 的模型文件放入

resnet50模型文件名:resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

百度就可以找到下载放入即可:https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

2.运行开发环境

进入 "垃圾分类-本地训练"目录

环境初始化

  • python3
  • 安装框架flaskpip3 install flask
  • 安装tensorflow,keras等依赖
  • pip3 install tensorflow==1.13.1
  • pip3 install keras==2.3.1

运行

  • 1.命令python3 train.py开启训练
  • 2.命令python3 predict_local.py开启输入图片测试

3. 训练服务模型部署

进入 "垃圾分类-服务部署"目录

  1. output_model 目录存放的是本地训练完成导出的h5模型文件

  2. models 目录需要手动下载resnet50 的模型文件放入

    resnet50模型文件名:resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

    百度就可以找到下载放入即可:https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5

环境初始化

  • 安装框架flaskpip3 install flask
  • 安装tensorflow,keras等依赖
  • pip3 install tensorflow==1.13.1
  • pip3 install keras==2.3.1

运行

  • 1.命令python3 run.py开启窗口本地调试
  • 2.命令python3 flask_sever.py开启服务部署
  • 3.命令sh ./start.sh开启后台运行服务部署

4.树莓派界面搭建

基于nodejs electron-vue

强烈建议使用cnpm来安装nodejs库

进入 "树莓派端/garbage_desktop"目录

安装依赖

cnpm install

开发模式

cnpm run dev

打包发布

cnpm run build

5.树莓派端flask-api接口操作硬件

进入"进入 "树莓派端/garbage_app_sever"目录"

注意树莓派应该开启I2C,确保pca9685 I2C方式接入后可显示地址

命令:i2cdetect -y 1

查看 地址项 0x40是否已经接入树莓派

运行 python3 app_sever.py 或者 sh start.sh 启动

若提示缺少依赖:

  1. pip3 install adafruit-pca9685
  2. pip3 install flask
木兰宽松许可证, 第2版 木兰宽松许可证, 第2版 2020年1月 http://license.coscl.org.cn/MulanPSL2 您对“软件”的复制、使用、修改及分发受木兰宽松许可证,第2版(“本许可证”)的如下条款的约束: 0. 定义 “软件”是指由“贡献”构成的许可在“本许可证”下的程序和相关文档的集合。 “贡献”是指由任一“贡献者”许可在“本许可证”下的受版权法保护的作品。 “贡献者”是指将受版权法保护的作品许可在“本许可证”下的自然人或“法人实体”。 “法人实体”是指提交贡献的机构及其“关联实体”。 “关联实体”是指,对“本许可证”下的行为方而言,控制、受控制或与其共同受控制的机构,此处的控制是指有受控方或共同受控方至少50%直接或间接的投票权、资金或其他有价证券。 1. 授予版权许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的版权许可,您可以复制、使用、修改、分发其“贡献”,不论修改与否。 2. 授予专利许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的(根据本条规定撤销除外)专利许可,供您制造、委托制造、使用、许诺销售、销售、进口其“贡献”或以其他方式转移其“贡献”。前述专利许可仅限于“贡献者”现在或将来拥有或控制的其“贡献”本身或其“贡献”与许可“贡献”时的“软件”结合而将必然会侵犯的专利权利要求,不包括对“贡献”的修改或包含“贡献”的其他结合。如果您或您的“关联实体”直接或间接地,就“软件”或其中的“贡献”对任何人发起专利侵权诉讼(包括反诉或交叉诉讼)或其他专利维权行动,指控其侵犯专利权,则“本许可证”授予您对“软件”的专利许可自您提起诉讼或发起维权行动之日终止。 3. 无商标许可 “本许可证”不提供对“贡献者”的商品名称、商标、服务标志或产品名称的商标许可,但您为满足第4条规定的声明义务而必须使用除外。 4. 分发限制 您可以在任何媒介中将“软件”以源程序形式或可执行形式重新分发,不论修改与否,但您必须向接收者提供“本许可证”的副本,并保留“软件”中的版权、商标、专利及免责声明。 5. 免责声明与责任限制 “软件”及其中的“贡献”在提供时不带任何明示或默示的担保。在任何情况下,“贡献者”或版权所有者不对任何人因使用“软件”或其中的“贡献”而引发的任何直接或间接损失承担责任,不论因何种原因导致或者基于何种法律理论,即使其曾被建议有此种损失的可能性。 6. 语言 “本许可证”以中英文双语表述,中英文版本具有同等法律效力。如果中英文版本存在任何冲突不一致,以中文版为准。 条款结束 如何将木兰宽松许可证,第2版,应用到您的软件 如果您希望将木兰宽松许可证,第2版,应用到您的新软件,为了方便接收者查阅,建议您完成如下三步: 1, 请您补充如下声明中的空白,包括软件名、软件的首次发表年份以及您作为版权人的名字; 2, 请您在软件包的一级目录下创建以“LICENSE”为名的文件,将整个许可证文本放入该文件中; 3, 请将如下声明文本放入每个源文件的头部注释中。 Copyright (c) [Year] [name of copyright holder] [Software Name] is licensed under Mulan PSL v2. 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简介

基于树莓派的垃圾分类识别,师院17级小透明投稿备份来自汤老师的《物联网项目规划与实施》课程期末作业展示。 展开 收起
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