521314 Goto Github PK

diaspora

A privacy-aware, distributed, open source social network.

directionalscalper

CCXT based algorithmic trading bot for Bybit, Binance, Bitget, and Huobi

django

The Web framework for perfectionists with deadlines.

docopt

Pythonic command line arguments parser, that will make you smile

documents

Thenextquant系列教程文档。

draw.io

Source to www.draw.io

drizzledumper

drizzleDumper是一款基于内存搜索的Android脱壳工具。

ea-python

量化交易策略

easypr

An open source project for Chinese plate recognition in unconstrained situation. It aims to be Easy, Flexible, and Accurate. Welcome to contribute your expertise !

easyquotation

实时获取新浪 / Leverfun 的免费股票以及 level2 十档行情 / 集思路的分级基金行情

easytrader

提供券银河／华泰客户端/雪球的基金、股票自动程序化交易以及自动打新，支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件

elements

Open Source implementation of advanced blockchain features extending the Bitcoin protocol

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.

eqgrp

Decrypted content of eqgrp-auction-file.tar.xz

eqgrp-free-file

Free sampling of files from the purported Equation Group hack.

eth

fabrik

Fabrik – Collaboratively build, visualize, and design neural nets in the browser

face-alignment

:fire: 2D and 3D Face alignment library build using pytorch

face-identification-tpe

Face identification with CNN + TPE using Keras

faceai

一款入门级的人脸、视频、文字检测以及识别的项目.

facebook

A Facebook Graph API SDK Library For Golang

facenet-face-recognition

A face recognition demo performed by feeding images of faces recorded by a webcam into a trained FaceNet network to determine the identity of the face

facerank

FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank-人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型（可能是最有趣的 TensorFlow 中文入门实战项目）（QQ群：522785813）。

facetools

一键人脸归一化处理工具，包括人脸检测，人脸关键点检测，基于关键点的人脸对齐

facets

Visualizations for machine learning datasets

face_recognition

The world's simplest facial recognition api for Python and the command line

facial-keypoints-detection

facial_detection

fastdex

加快 apk 的编译速度

fetch

A window.fetch JavaScript polyfill.