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atomspace

The OpenCog hypergraph database, query system and rule engine

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

carla

Open-source simulator for autonomous driving research.

cicd-apiserver

Example of Kubernetes Aggregated API Server

despot

The DESPOT online POMDP solver

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.

etcd

Distributed reliable key-value store for the most critical data of a distributed system

evo

Python package for the evaluation of odometry and SLAM

face_recognition

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

firstpassion.github.io

flannel

flannel is a network fabric for containers, designed for Kubernetes

hello_world

my first code

hexo-theme-yilia-plus

一个简洁优雅的hexo主题 A simple and elegant theme for hexo.

image-analogies

Generate image analogies using neural matching and blending.

imageai

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

instance-segmentation-with-discriminative-loss-tensorflow

Tensorflow implementation of "Semantic Instance Segmentation with a Discriminative Loss Function"

intentionprediction

Code to predict the intention of human drivers on the approach to intersections

koko

KoKo 是 JumpServer 字符协议资产安全连接组件，支持 SSH, Telnet, MySQL, Kubernets, SFTP, SQL Server, Redis

kubevirt

Kubernetes Virtualization API and runtime in order to define and manage virtual machines.

lstm-human-activity-recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier

machine-learning

this is my processing of machine learning

mdn-lstm-for-prediction

newdas

ngsim_env

An rllab environment for learning human driver models with imitation learning using NGSIM data

pymdptoolbox

Markov Decision Process (MDP) Toolbox for Python

python

pythonrobotics

Python sample codes for robotics algorithms.

rnn-tutorial-gru-lstm

Language Model GRU with Python and Theano

scheduler-plugins

Repository for out-of-tree scheduler plugins based on scheduler framework.

scikit-image

Image processing in Python