woderia's Stars
yidao620c/python3-cookbook
《Python Cookbook》 3rd Edition Translation
justinzm/gopup
数据接口：百度、谷歌、头条、微博指数,宏观数据，利率数据，货币汇率，千里马、独角兽公司，新闻联播文字稿，影视票房数据，高校名单，疫情数据…
iamseancheney/python_for_data_analysis_2nd_chinese_version
《利用Python进行数据分析·第2版》
shengcaishizhan/kkndme_tianya
天涯 kkndme 神贴聊房价
apachecn/ailearning
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
ben1234560/AiLearning-Theory-Applying
快速上手AI理论及应用实战：基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集，力求每一位能看懂并复现。
jackzhenguo/python-small-examples
告别枯燥，致力于打造 Python 实用小例子，更多Python良心教程见 https://ai-jupyter.com
mbrn/material-table
Datatable for React based on material-ui's table with additional features
apache/pinot
Apache Pinot - A realtime distributed OLAP datastore
will4906/CaptchaRecognition
验证码识别
ageitgey/face_recognition
The world's simplest facial recognition api for Python and the command line
vipstone/faceai
一款入门级的人脸、视频、文字检测以及识别的项目.
gyh1621/GetSubtitles
一步下载匹配字幕
ausaki/subfinder
字幕查找器
7sDream/zhihu-py3
[不再维护] 后继者 zhihu-oauth https://github.com/7sDream/zhihu-oauth 已被 DMCA，亦不再开发，仅提供代码存档：
Pelhans/Z_knowledge_graph
Bulding kg from 0
rockingdingo/deepnlp
Deep Learning NLP Pipeline implemented on Tensorflow
hclander/scene_split
Simple video splitter to deal with PySceneDetect csv output
Breakthrough/PySceneDetect
:movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
dhvanikotak/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.
krichter722/video-splitter
Automated scene splitt clip extraction based on melt
vdaubry/html5-video-splitter
A simple web app to split a video
c0decracker/video-splitter
Simple Python script to split video into equal length chunks or chunks of equal size, duration, etc.
striver-ing/headlines_today
基于Python的爬取今日头条文章及视频
zjfGit/python3-scrapy-spider-phantomjs-selenium
基于Python3的动态网站爬虫，使用selenium+phantomjs实现爬取动态网站, 本项目以爬取今日头条为例
fourbrother/python_toutiaovideo
python脚本爬取今日头条视频数据
yl-L/tumblr_spider
python3-tumblr-爬虫，通过api接口，多线程同时下载下载图片以及视频资源
ginping/Instagram_crawler
Python爬虫爬取 Instagram 博主照片视频
loadchange/amemv-crawler
🙌Easily download all the videos from TikTok(amemv).下载指定的 抖音（Douyin） 号的视频,抖音爬虫
514840279/danyuan-application
初学者 spirng-boot版本