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doctorai

Repository for Doctor AI project

doga

:chart_with_upwards_trend: HTTP log monitoring console for Humans

doraemon

Doraemon-接口自动化测试工具

dotenv-linter

:relaxed: Linting dotenv files like a charm!

dottorrent-gui

An advanced GUI torrent file creator with batch functionality, powered by PyQt and dottorrent

dpc

Video Representation Learning by Dense Predictive Coding. Tengda Han, Weidi Xie, Andrew Zisserman.

dqn_hollowknight

drf-haystack

Haystack for Django REST Framework

drf-pyotp

pyotp extension for Django Rest Framework

dropbox-restore

Restore any dropbox folder to a previous state

dsrg

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

dss

Differentiable Surface Splatting

dsvr

Domain-Specific-VPN-Router

dualgan

DualGAN-tensorflow: tensorflow implementation of DualGAN

dualrl

A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer (IJCAI 2019)

dubbo-python

Python Client For Apache Dubbo

dumpflash

Low-level NAND Flash dump and parsing utility

dvr-scan

:vhs: Tool for extracting scenes with motion from videos (e.g. security camera or DVR footage). Written in Python, uses OpenCV.

e2e-tbsa

A Unified Model for Opinion Target Extraction and Target Sentiment Prediction (AAAI 2019)

eagermot

Official code for "EagerMOT: 3D Multi-Object Tracking via Sensor Fusion" [ICRA 2021]

easy-node-authentication

Code for the entire scotch.io tutorial series: Complete Guide to Node Authentication

eegsynth

Converting real-time EEG into sounds, music and visual effects

elasticutils

[deprecated] A friendly chainable ElasticSearch interface for python

electron-inject

Inject javascript into closed source electron applications e.g. to enable developer tools for debugging.

elfi

ELFI - Engine for Likelihood-Free Inference

elijahahianyo

my personal page

elite-proxy-finder

Finds public elite anonymity proxies and concurrently tests them

emojione-picker-ubuntu

Emoji picker for Ubuntu based on icons by Emojione

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.

enaml-native

Build native mobile apps in python with enaml