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30daymakecppserver

30天自制C++服务器，包含教程和源代码

all-in-

awesome_dnn_researchers

We will introduce the researchers who made great contributions to DNNs in the projects.

beautiful_linux

Linux的美化方法

bgslibrary

A C++ Background Subtraction Library with wrappers for Python, MATLAB, Java and GUI on QT

cloudpan

linux下c/c++模拟实现云盘项目

cnn-facial-landmark

Facial landmark detection based on deep convolutional neural network.

coder-kung-fu

开发内功修炼

cppguide

C/C++学习，后端开发进阶指南。

cs-notes

:books: Tech Interview Guide 技术面试必备基础知识、Leetcode 题解、Java、C++、Python、后端面试、操作系统、计算机网络、系统设计

data-structures-and-algorithms

用C++语言实现数据结构和算法，包括排序算法、二叉堆、二叉搜索树、图、最短路径、最小生成树

deeplearning-models

各种深度学习结构、模型和技巧的集合

deeplearningsystem

Deep Learning System core principles introduction.

dl-engineer-guidebook

深度学习工程师生存指南

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.

face2face-demo

pix2pix demo that learns from facial landmarks and translates this into a face

facelogin

:man: 使用 OpenCV 和 Qt 实现人脸（刷脸）登录

facerecognition

利用OpenCV、CNN进行人脸识别

facial-expression-recognition.pytorch

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

flamingo

flamingo 一款高性能轻量级开源即时通讯软件

flvanalyser

FLV v1.0 analyser

grml-zsh

Mirrored from grml.org/zsh/zshrc, with added flavoring.

hello-algorithm

🌍🌎🌏 东半球最酷的学习项目┃包括：1、我写的三十万字图解算法题典 2、100 张各语言思维导图 和 1000 本编程电子📚 3、100 篇大厂面经下载┃ English version supported !!! 国人项目上榜不易，右上角助力一波！干就对了，奥利给 ！🚀🚀

hellogithub

:octocat: Find pearls on open-source seashore 分享 GitHub 上有趣、入门级的开源项目

hrnet-facial-landmark-detection

High-resolution representation learning (HRNets) for facial landmark detection

huawei-codecraft-2019

2019华为软件精英挑战赛，杭夏赛区-咕咕咕，决赛冠军

huawei2019codecraft

2019年华为软件精英挑战赛代码仓储，最美代码奖，初赛复赛西北第一，决赛32强

interview

📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。

introduction-to-golang

【未来服务器端编程语言】最全空降golang资料补给包（满血战斗），包含文章，书籍，作者论文，理论分析，开源框架，云原生，大佬视频，大厂实战分享ppt

kratos

Your ultimate Go microservices framework for the cloud-native era.