woderia

CBoard

Open BI dashboard platform

convertVideo

解析今日头条、西瓜视频、内涵段子、阳光宽频网视频

crawl_wechat

用于批量爬取微信公众号所有文章

douyin

微信小程序+springboot仿造抖音视频练习

drone

light-weight hive query workbench

DWSurvey

DWSurvey 调问—表单问卷系统

easydocs

a easy docs

elasticsearch-sentiment

基于ElasticSearch的分布式舆情检索统计服务。

Emotion-Detection-in-Videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

FineReport

woderia/douyin

微信小程序+springboot仿造抖音视频练习

woderia/CBoard

Open BI dashboard platform

woderia/convertVideo

解析今日头条、西瓜视频、内涵段子、阳光宽频网视频

woderia/crawl_wechat

用于批量爬取微信公众号所有文章

woderia/DWSurvey

DWSurvey 调问—表单问卷系统

woderia/Emotion-Detection-in-Videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

woderia/FineReport

woderia/javaCrawling

"奇伢爬虫"是基于sprint boot 、 WebMagic 实现 微信公众号文章、新闻、csdn、info等网站文章爬取，可以动态设置文章爬取规则、清洗规则，基本实现了爬取大部分网站的文章。

woderia/m-mall

微信小程序-小商城前台

woderia/m-mall-admin

微信小程序-小商城后台

woderia/MeiZiImageDownLoader

一个妹纸网的图片爬虫,基于webmagic写的。

woderia/node-spider

nodejs爬取西瓜视频（今日头条视频）

woderia/okcoin-leeks-reaper

OKCoin韭菜收割机

woderia/pivot

A data exploration UI for Druid

woderia/python-spider

:rainbow:Python3网络爬虫实战：VIP视频破解助手；GEETEST验证码破解；小说、动漫下载；手机APP爬取；财务报表入库；火车票抢票；抖音APP视频下载；百万英雄辅助；网易云音乐下载；B站视频和弹幕下载；京东晒单图下载

woderia/ResumeSample

Resume template for Chinese programmers . 程序员简历模板系列。包括PHP程序员简历模板、iOS程序员简历模板、Android程序员简历模板、Web前端程序员简历模板、Java程序员简历模板、C/C++程序员简历模板、NodeJS程序员简历模板、架构师简历模板以及通用程序员简历模板

woderia/ruyi

交互式表单设计器

woderia/sensitive-stop-words

互联网常用敏感词、停止词词库

woderia/spider_webmagic

微小宝微信公众号文章爬取

woderia/Spring-Boot-Neo4j-Movies

Spring-Boot集成Neo4j并利用Spark的朴素贝叶斯分类器实现基于电影知识图谱的智能问答系统

woderia/TianLiangSpider4YuQing

天亮舆情系统之天亮舆情采集器，基于master/slave结构开发的分布采集器系统

woderia/video-splitter

Simple Python script to split video into equal length chunks or chunks of equal size, duration, etc.

woderia/video-splitter-1

Automated scene splitt clip extraction based on melt

woderia/WickedGrid

Easy & Wicked Fast spreadsheets for the web

woderia/wx_calendar

微信小程序－日历 📅

woderia/WXVideo_server_admin

微信短视频管理端

woderia/WXVideos-wxclient

微信短视频小程序端

woderia/xigua_video

西瓜(头条)视频下载

woderia/Z_knowledge_graph

Bulding kg from 0

woderia/zeus