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jackylee1's Projects

amazon-rekognition-video-analyzer icon amazon-rekognition-video-analyzer

A working prototype for capturing frames off of a live MJPEG video stream, identifying objects in near real-time using deep learning, and triggering actions based on an objects watch list.

amazon-sumerian-arkit-starter-app icon amazon-sumerian-arkit-starter-app

A sample XCode project that demonstrates how to create a simple augmented reality experience using Apple's ARKit with the Amazon Sumerian service

amazon_spider_sys icon amazon_spider_sys

Amazon Spider 亚马逊商品信息抓取系统,包含商品监控模块,商品评价监控模块,商品库存监控系统,评论词云模块,用户管理模块

amazoncamera icon amazoncamera

[iOS] Photo management app using Amazon Web Services (AWS)

android icon android

GitHub上最火的Android开源项目,所有开源项目都有详细资料和配套视频

android-courierassistant icon android-courierassistant

高峰期时,快递员一天平均派送100多快件,工作时间超13小时。快递员的一天始于分拣快递,分拣是最重要的一步。快递员分拣出自己需要派送的快件,分拣完了心里就大概清楚要走的路线。在派送的过程中,由于快件过多,快递员通常会错派,漏派,导致走冤枉路,降低效率。并且通常快递员心里计算的路线和最优规划路线差距很大,走不熟悉路线时没有一键导航,导致绕路、走错路。快递员到达快件目的地楼下后再拨打客户电话通知取件,需要等待客户来取件,等待的平均时间超过5分钟。且客户有可能这个时间段不在,快递员就白送了一趟。 本项目基于以上实际情况,开发出一款Android App——“派送助手”。首先将快件的地址和电话信息添加到“派送助手”,点击“开始派送”,后台将会通过后台算法计算最优路线,确定快件的派送次序,快递员依照提示依次派送,点击“导航”可以获取到达下一目的地的导航,当将要到达目的地时,手机会智能识别并提前拨叫用户。节省快递员等待客户取件的时间。普通客户可以同关注派送助手微信公众号实时接收到快件信息,可以通过微信公众号设置方便签收的时间或设置代收。快递员将会在规定时间段将快递送给客户。

anime-gan-tensorflow icon anime-gan-tensorflow

The BIGGAN based Anime generation implemented with tensorflow. All training data has been open sourced.

app.lara-shop icon app.lara-shop

一个基于vue的app商城项目,不可思议的简洁卡片式风格

ar.js icon ar.js

Efficient Augmented Reality for the Web - 60fps on mobile!

ask-anything icon ask-anything

a simple yet interesting tool for chatting about video with chatGPT and miniGPT4

assembly-learning icon assembly-learning

记录汇编语言学习中的有趣点,参考书籍《汇编语言第三版 王爽著》

attgan-tensorflow icon attgan-tensorflow

AttGAN Tensorflow Arbitrary Facial Attribute Editing: Only Change What You Want

avatarify icon avatarify

Avatars for Zoom, Skype and other video-conferencing apps.

avatarify-1 icon avatarify-1

It means to reciprocate the motion from video to human face and looks like the real man talking video. In this module you will find the code implementation , documentation file for the model working and results.

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